Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions

Uncovering hidden subtypes in dementia: An unsupervised machine learning approach to dementia diagnosis and personalization of care

OBJECTIVE

Dementia represents a growing public health challenge, affecting an increasing number of individuals. It encompasses a broad spectrum of cognitive impairments, ranging from mild to severe stages, each of which demands varying levels of care. Current diagnostic approaches often treat dementia as a uniform condition, potentially overlooking clinically significant subtypes, which limits the effectiveness of treatment and care strategies. This study seeks to address the limitations of traditional diagnostic methods by applying unsupervised machine learning techniques to a large, multi-modal dataset of dementia patients (encompassing multiple data sources including clinical, demographic, gene expression and protein concentrations), with the aim of identifying distinct subtypes within the population. The primary focus is on differentiating between mild and severe stages of dementia to improve diagnostic accuracy and personalize treatment plans.

METHODS

The dataset analyzed included 911 individuals, described by 157 multi-modal characteristics, encompassing clinical, genomic, proteomic and sociodemographic features. After handling missing data, the dataset was reduced to 561 rows and 135 columns. Various dimensionality reduction techniques were applied to improve the feature-to-patient ratio, and unsupervised clustering methods were employed to identify potential subtypes. The major novelty in our methodology regards the combination of different techniques, bridging high-dimensional statistical inference, multi-modal dimensionality reduction and clustering analysis, to appropriately model the multi-modal nature of the data and ensure clinical relevance.

RESULTS

The analysis revealed distinct clusters within the dementia population, each characterized by specific clinical and demographic profiles. These profiles included variations in biomarkers, cognitive scores, and disability levels. The findings suggest the presence of previously unrecognized subgroups, distinguished by their genomic, proteomic, and clinical characteristics.

CONCLUSION

This study demonstrates that unsupervised machine learning can effectively identify clinically relevant subtypes of dementia, with important implications for diagnosis and personalized treatment. Further research is required to validate these findings and investigate their potential to improve patient outcomes.

Conference report: Trends, new technologies and implications for dementia diagnostics, treatment and care in Switzerland

Dementia diseases represent a major burden for the directly affected people, their relatives and modern society. Despite considerable efforts in recent years, early and accurate disease diagnosis and monitoring is still a challenge while no cure is available in most cases. New drugs, in particular disease-modifying therapies, and recent technological advancements offer promising perspectives. The integration of novel biomarkers, artificial intelligence and digital health tools has the potential to transform dementia care, making it more personalised, efficient and adapted to the living conditions and needs of older people. In November 2023, the 7th Dementia Summit convened a panel of experts from geriatrics, neurology, neuropsychology, psychiatry, ethics as well as general medicine to discuss interdisciplinary challenges, advancements and their implications for the future of dementia care in Switzerland. The conference underscored the importance of a multidisciplinary approach to successfully integrate new technologies in both clinical-translational research and dementia prevention, diagnosis and care. While recent innovations represent major steps forward, their implementation also comes with important challenges including questions on healthcare system preparedness and adaptation, ethical aspects, technology literacy, acceptance and appropriate use.

Evaluating ChatGPT for neurocognitive disorder diagnosis: a multicenter study

Objective: To evaluate the accuracy and reliability of ChatGPT 4 Omni in diagnosing neurocognitive disorders using comprehensive clinical data and compare its performance to previous versions of ChatGPT. Method: This project utilized a two-part design: Study 1 examined diagnostic agreement between ChatGPT 4 Omni and clinicians using a few-shot prompt approach, and Study 2 compared the diagnostic performance of ChatGPT models using a zero-shot prompt approach using data from the National Alzheimer's Coordinating Center (NACC) Uniform Data Set 3. Study 1 included 12,922 older adults (Mage = 69.13, SD = 9.87), predominantly female (57%) and White (80%). Study 2 involved 537 older adults (Mage = 67.88, SD = 9.52), majority female (57%) and White (81%). Diagnoses included no cognitive impairment, amnestic mild cognitive impairment (MCI), nonamnestic MCI, and dementia. Results: In Study 1, ChatGPT 4 Omni showed fair association with clinician diagnoses (χ2 (9) = 6021.96, p < .001; κ = .33). Notable predictive measures of agreement included the MoCA and memory recall tests. ChatGPT 4 Omni demonstrated high internal reliability (α = .96). In Study 2, no significant diagnostic agreement was found between ChatGPT versions and clinicians. Conclusions: Although ChatGPT 4 Omni shows potential in aligning with clinician diagnoses, its diagnostic accuracy is insufficient for clinical application without human oversight. Continued refinement and comprehensive training of AI models are essential to enhance their utility in neuropsychological assessment. With rapidly developing technological innovations, integrating AI tools in clinical practice could soon improve diagnostic efficiency and accessibility to neuropsychological services.

Incremental Value of Multidomain Risk Factors for Dementia Prediction: A Machine Learning Approach

OBJECTIVE

The current evidence regarding how different predictor domains contributes to predicting incident dementia remains unclear. This study aims to assess the incremental value of five predictor domains when added to a simple dementia risk prediction model (DRPM) for predicting incident dementia in older adults.

DESIGN

Population-based, prospective cohort study.

SETTING

UK Biobank study.

PARTICIPANTS

Individuals aged 60 or older without dementia.

MEASUREMENTS

Fifty-five dementia-related predictors were gathered and categorized into clinical and medical history, questionnaire, cognition, polygenetic risk, and neuroimaging domains. Incident dementia (all-cause) and the subtypes, Alzheimer's disease (AD) and vascular dementia (VaD), were determined through hospital and death registries. Ensemble machine learning (ML) DRPMs were employed for prediction. The incremental values of risk predictors were assessed using the percent change in Area Under the Curve (∆AUC%) and the net reclassification index (NRI).

RESULTS

The simple DRPM which included age, body mass index, sex, education, diabetes, hyperlipidaemia, hypertension, depression, smoking, and alcohol consumption yielded an AUC of 0.711 (± 0.008 SD). The five predictor domains exhibited varying levels of incremental value over the basic model when predicting all-cause dementia and the two subtypes. Neuroimaging markers provided the highest incremental value in predicting all-cause dementia (∆AUC% +9.6%) and AD (∆AUC% +16.5%) while clinical and medical history data performed the best at predicting VaD (∆AUC% +12.2%). Combining clinical and medical history, and questionnaire data synergistically enhanced ML DRPM performance.

CONCLUSION

Combining predictors from different domains generally results in better predictive performance. Selecting predictors involves trade-offs, and while neuroimaging markers can significantly enhance predictive accuracy, they may pose challenges in terms of cost or accessibility.

Boostering diagnosis of frontotemporal lobar degeneration with AI-driven neuroimaging - A systematic review and meta-analysis

BACKGROUND AND OBJECTIVES

Frontotemporal lobar degeneration (FTLD) as the second most common dementia encompasses a range of syndromes and often shows overlapping symptoms with other subtypes or neurodegenerative diseases, which poses a significant clinical diagnostic challenge. Recent advancements in artificial intelligence (AI), specifically the application of machine learning (ML) algorithms to neuroimaging, have significantly progressed in addressing this challenge. This study aims to assess the diagnostic and predictive efficacy of neuroimaging feature-based AI algorithms for FTLD.

METHODS

We conducted a systematic review and meta-analysis following PRISMA guidelines. We searched Pubmed, Scopus, and Web of Science for English-language, peer-reviewed studies using the following three umbrella terms: artificial intelligence, frontotemporal lobar degeneration, and neuroimaging modality. Our survey focused on computer-aided diagnosis for FTLD, employing machine/deep learning with neuroimaging radiomic features.

RESULTS

The meta-analysis includes 75 articles with 20,601 subjects, including 8,051 FTLD patients. The results reveal that FTLD can be automatically classified against healthy controls (HC) with pooled sensitivity and specificity of 86% and 89%, respectively. Likewise, FTLD versus Alzheimer's disease (AD) classification exhibits pooled sensitivity and specificity of 84% and 81%, while FTLD versus Parkinson's disease (PD) demonstrates pooled sensitivity and specificity of 84% and 75%, respectively. Classification performance distinguishing FTLD from atypical Parkinsonian syndromes (APS) showed pooled sensitivity and specificity of 84% and 79%, respectively. Multiclass classification sensitivity ranges from 42% to 100%, with lower sensitivity occurring in higher class distinctions (e.g., 5-class and 11-class).

DISCUSSION

Our study demonstrates the effectiveness of utilizing neuroimaging features to distinguish FTLD from HC, AD, APS, and PD in binary classification. Utilizing deep learning with multimodal neuroimaging data to differentiate FTLD subtypes and perform multiclassification among FTLD and other neurodegenerative disease holds promise for expediting diagnosis. In sum, the meta-analysis supports translation of machine learning tools in combination with imaging to clinical routine paving the way to precision medicine.

Obfuscation via pitch-shifting for balancing privacy and diagnostic utility in voice-based cognitive assessment

INTRODUCTION

Digital voice analysis is an emerging tool for differentiating cognitive states, but it poses privacy risks as automated systems may inadvertently identify speakers.

METHODS

We developed a computational framework to evaluate the trade-off between voice obfuscation and cognitive assessment accuracy, using pitch-shifting as a representative method. This framework was applied to voice recordings from the Framingham Heart Study (FHS, n = 128) and the DementiaBank Delaware (DBD, n = 85) corpus, both featuring responses to neuropsychological tests. Speaker obfuscation was measured via equal error rate (EER), and diagnostic utility was assessed through machine learning models distinguishing cognitive states: normal cognition (NC), mild cognitive impairment (MCI), and dementia (DE).

RESULTS

With the top 20 acoustic features, our framework achieved classification accuracies of 62.2% (EER: 0.3335) on the FHS dataset for NC, MCI, and DE differentiation, and 63.7% (EER: 0.1796) on the DBD dataset for NC and MCI differentiation, using obfuscated speech files.

DISCUSSION

Our results demonstrate the feasibility of privacy-preserving voice markers, offering a scalable solution for voice-based cognitive assessments.

HIGHLIGHTS

We developed a computational framework using pitch-shifting and acoustic transformations to balance speaker privacy and diagnostic utility in voice-based cognitive assessments. We evaluated the framework on two independent datasets, Framingham Heart Study (FHS, n = 128) and DementiaBank Delaware (DBD, n = 85) corpus, assessing the trade-off between privacy (measured by equal error rate [EER]) and classification accuracy. Our framework achieved classification accuracies of 62.2% (EER: 0.3335) for distinguishing normal cognition (NC), mild cognitive impairment (MCI), and dementia in the FHS dataset and 63.7% (EER: 0.1796) for NC and MCI differentiation in the DBD dataset, using obfuscated speech files. Our framework demonstrates that pitch-shifting levels can preserve diagnostic utility while protecting speaker identity, offering a scalable and privacy-preserving solution.

The application of machine learning in 3D/4D printed stimuli-responsive hydrogels

The integration of machine learning (ML) in materials fabrication has seen significant advancements in recent scientific innovations, particularly in the realm of 3D/4D printing. ML algorithms are crucial in optimizing the selection, design, functionalization, and high-throughput manufacturing of materials. Meanwhile, 3D/4D printing with responsive material components has increased the vast design flexibility for printed hydrogel composite materials with stimuli responsiveness. This review focuses on the significant developments in using ML in 3D/4D printing to create hydrogel composites that respond to stimuli. It discusses the molecular designs, theoretical calculations, and simulations underpinning these materials and explores the prospects of such technologies and materials. This innovative technological advancement will offer new design and fabrication opportunities in biosensors, mechatronics, flexible electronics, wearable devices, and intelligent biomedical devices. It also provides advantages such as rapid prototyping, cost-effectiveness, and minimal material wastage.

Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia

Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia. Data was sourced from the Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Clinical variables included 21 measures across medical history, hematological and other blood tests, and APOE genotype. Tree-based machine learning algorithms and artificial neural networks were used. APOE genotype was the best predictor of dementia cases and healthy controls. Our results, however, demonstrated that there are limitations when using publicly accessible cohort data that may limit the generalizability and interpretability of such predictive models. Future research should examine the use of routine APOE genetic testing for dementia diagnostics. It should also focus on clearly unifying data across clinical cohorts.

Physical activity and the outcome of cognitive trajectory: a machine learning approach

BACKGROUND

Physical activity (PA) may have an impact on cognitive function. Machine learning (ML) techniques are increasingly used in dementia research, e.g., for diagnosis and risk stratification. Less is known about the value of ML for predicting cognitive decline in people with dementia (PwD). The aim of this study was to use an ML approach to identify variables associated with a multimodal PA intervention that may impact cognitive changes in PwD, i.e., by distinguishing between cognitive decliners and non-decliners.

METHODS

This is a secondary, exploratory analysis using data from a Randomized Controlled Trial that included a 16-week multimodal PA intervention for the intervention group (IG) and treatment as usual for the control group (CG) in nursing homes. Predictors included in the ML models were related to the intervention (e.g., adherence), physical performance (e.g., mobility, balance), and pertinent health-related variables (e.g., health status, dementia form and severity). Primary outcomes were global and domain-specific cognitive performance (i.e., attention/ executive function, language, visuospatial skills, memory) assessed by standardized tests. A Support Vector Machine model was used to perform the classification of each primary outcome into the two classes of decline and non-decline. GridSearchCV with fivefold cross-validation was used for model training, and area under the ROC curve (AUC) and accuracy were calculated to assess model performance.

RESULTS

The study sample consisted of 319 PwD (IG, N = 161; CG, N = 158). The proportion of PwD experiencing cognitive decline, in the different domains measured, ranged from 27-48% in CG, and from 23-49% in IG, with no statistically significant differences and no time*group effects. ML models showed accuracy and AUC values ranging from 40.6-75.6. The strongest predictors of cognitive decline or non-decline were performance of activities of daily living in IG and CG, and adherence and mobility in IG.

CONCLUSIONS

ML models showed moderate performance, suggesting that the selected variables only had limited value for classification, with adherence and performance of activities of daily living appearing to be predictors of cognitive decline. While the study provides preliminary evidence of the potential use of ML approaches, larger studies are needed to confirm our observations and to include other variables in the prediction of cognitive decline, such as emotional health or biomarker abnormalities.

Prediction of cognitive impairment among Medicare beneficiaries using a machine learning approach

OBJECTIVE

Developing machine learning (ML) models to predict cognitive impairment among Medicare beneficiaries in the United States.

METHODS

This retrospective study used the 2016-2019 Medicare Current Beneficiary Survey Cost and Use and Survey Public Use Files. Medicare beneficiaries aged 65 and older (n=4,965) with at least two consecutive years' data were included. Cognitive impairment was categorized into three stages: severe, moderate, and none based on self-reported data. Baseline year's demographic, socioeconomic factors, self-reported functional limitations, health status and comorbidities, number of concurrent medications, level of social engagement, behavioral variables, and satisfaction of medical care's quality were features assessed in ML algorithms to predict next years' cognitive function. ML models in six major categories were developed, tested, and compared (accuracy, AUC, and F1 score) using Python version 3.11. The importance of features was evaluated using the total reduction of the Gini. A subgroup analysis was conducted among beneficiaries who were 80 years and older.

RESULTS

Approximately 11.1% of beneficiaries aged ≥ 65 had moderate or severe cognitive function impairment. Baseline cognitive function was the most significant predictor for next year's cognitive function impairment, followed by baseline IADL, level of social activities, ADL, general health status, income, age, education, region of residence, and body mass index. Beneficiaries 80 years and older had satisfaction of medical care's quality among the top 10 most significant predictors.

CONCLUSIONS

Older adults' baseline cognitive function and IADL were top two predictors of cognitive function impairment. Clinicians should regularly screen and monitor older adults' cognitive and daily function.

Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis

BACKGROUND

To diagnose Alzheimer disease (AD), individuals are classified according to the severity of their cognitive impairment. There are currently no specific causes or conditions for this disease.

OBJECTIVE

The purpose of this systematic review and meta-analysis was to assess AD prevalence across different stages using machine learning (ML) approaches comprehensively.

METHODS

The selection of papers was conducted in 3 phases, as per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelines: identification, screening, and final inclusion. The final analysis included 24 papers that met the criteria. The selection of ML approaches for AD diagnosis was rigorously based on their relevance to the investigation. The prevalence of patients with AD at 2, 3, 4, and 6 stages was illustrated through the use of forest plots.

RESULTS

The prevalence rate for both cognitively normal (CN) and AD across 6 studies was 49.28% (95% CI 46.12%-52.45%; P=.32). The prevalence estimate for the 3 stages of cognitive impairment (CN, mild cognitive impairment, and AD) is 29.75% (95% CI 25.11%-34.84%, P<.001). Among 5 studies with 14,839 participants, the analysis of 4 stages (nondemented, moderately demented, mildly demented, and AD) found an overall prevalence of 13.13% (95% CI 3.75%-36.66%; P<.001). In addition, 4 studies involving 3819 participants estimated the prevalence of 6 stages (CN, significant memory concern, early mild cognitive impairment, mild cognitive impairment, late mild cognitive impairment, and AD), yielding a prevalence of 23.75% (95% CI 12.22%-41.12%; P<.001).

CONCLUSIONS

The significant heterogeneity observed across studies reveals that demographic and setting characteristics are responsible for the impact on AD prevalence estimates. This study shows how ML approaches can be used to describe AD prevalence across different stages, which provides valuable insights for future research.

Can machine learning assist us in the classification of older patients suffering from dementia based on classic neuropsychological tests and a new financial capacity test performance?

AIMS

Predicting the diagnosis of an older adult solely based on their financial capacity performance or other neuropsychological test performance is still an open question. The aim of this study is to highlight which tests are of importance in diagnostic protocols by using recent advancements in machine learning.

METHODS

For this reason, a neuropsychological battery was administered in 543 older Greek patients already diagnosed with different types of neurocognitive disorders along with a test specifically measuring financial capacity, that is, Legal Capacity for Property Law Transactions Assessment Scale (LCPLTAS). The battery was analysed using a random forest algorithm. The objective was to predict whether an older person suffers from dementia. The algorithm's performance was tested through cross-validation.

RESULTS

Machine learning was applied for the first time in data analysis regarding financial capacity and three factors-tests were revealed as the best predictors: two subscales from the LCPLTAS measuring 'financial decision making' and 'cash transactions', and the widely used MMSE which measures general cognition. The algorithm demonstrated good performance as measured by the F1-score, which is a measure of the harmonic mean of precision and recall. This evaluation metric in binary and multi-class classification integrates precision and recall into a single metric to gain a better understanding of model performance.

CONCLUSIONS

These findings reveal the importance of focusing on these scales and tests in neuropsychological assessment protocols. Future research may clarify in other cultural settings if the same variables are of importance.

Random survival forest model for early prediction of Alzheimer's disease conversion in early and late Mild cognitive impairment stages

With a clinical trial failure rate of 99.6% for Alzheimer's Disease (AD), early diagnosis is critical. Machine learning (ML) models have shown promising results in early AD prediction, with survival ML models outperforming typical classifiers by providing probabilities of disease progression over time. This study utilized various ML survival models to predict the time-to-conversion to AD for early (eMCI) and late (lMCI) Mild Cognitive Impairment stages, considering their different progression rates. ADNI data, consisting of 291 eMCI and 546 lMCI cases, was preprocessed to handle missing values and data imbalance. The models used included Random Survival Forest (RSF), Extra Survival Trees (XST), Gradient Boosting (GB), Survival Tree (ST), Cox-net, and Cox Proportional Hazard (CoxPH). We evaluated cognitive, cerebrospinal fluid (CSF) biomarkers, and neuroimaging modalities, both individually and combined, to identify the most influential features. Our results indicate that RSF outperformed traditional CoxPH and other ML models. For eMCI, RSF trained on multimodal data achieved a C-Index of 0.90 and an IBS of 0.10. For lMCI, the C-Index was 0.82 and the IBS was 0.16. Cognitive tests showed a statistically significant improvement over other modalities, underscoring their reliability in early prediction. Furthermore, RSF-generated individual survival curves from baseline data facilitate clinical decision-making, aiding clinicians in developing personalized treatment plans and implementing preventive measures to slow or prevent AD progression in prodromal stages.

Beyond group classification: Probabilistic differential diagnosis of frontotemporal dementia and Alzheimer's disease with MRI and CSF biomarkers

Neuroimaging and fluid biomarkers are used to differentiate frontotemporal dementia (FTD) from Alzheimer's disease (AD). We implemented a machine learning algorithm that provides individual probabilistic scores based on magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. We investigated whether combining MRI and CSF levels could improve the diagnosis confidence. 215 AD patients, 103 FTD patients, and 173 healthy controls (CTR) were studied. With MRI data, we obtained an accuracy of 82 % for AD vs. FTD. A total of 74 % of FTD and 73 % of AD participants have a high probability of accurate diagnosis. Adding CSF-NfL and 14-3-3 levels improved the accuracy and the number of patients in the confidence group for differentiating FTD from AD. We obtain individual diagnostic probabilities with high precision to address the problem of confidence in the diagnosis. We suggest when MRI, CSF, or the combination are necessary to improve the FTD and AD diagnosis. This algorithm holds promise towards clinical applications as support to clinical findings or in settings with limited access to expert diagnoses.

Obfuscation via pitch-shifting for balancing privacy and diagnostic utility in voice-based cognitive assessment

Introduction

Digital voice analysis is gaining traction as a tool to differentiate cognitively normal from impaired individuals. However, voice data poses privacy risks due to the potential identification of speakers by automated systems.

Methods

We developed a framework that uses weighted linear interpolation of privacy and utility metrics to balance speaker obfuscation and cognitive integrity in cognitive assessments. This framework applies pitch-shifting for speaker obfuscation while preserving cognitive speech features. We tested it on digital voice recordings from the Framingham Heart Study (N=128) and Dementia Bank Delaware corpus (N=85), both containing responses to neuropsychological tests.

Results

The tool effectively obfuscated speaker identity while maintaining cognitive feature integrity, achieving an accuracy of 0.6465 in classifying individuals with normal cognition, mild cognitive impairment, and dementia in the FHS cohort.

Discussion

Our approach enables the development of digital markers for dementia assessment while protecting sensitive personal information, offering a scalable solution for privacy-preserving voice-based diagnostics.

Optimizing Machine Learning Models for Accessible Early Cognitive Impairment Prediction: A Novel Cost-effective Model Selection Algorithm

Cognitive impairment and dementia-related diseases develop several years before moderate or severe deterioration in cognitive function occurs. Nevertheless, most dementia cases, especially in low- and middle-income countries, remain undiagnosed because of limited access to affordable diagnostic tools. Additionally, the development of accessible tools for diagnosing and predicting cognitive impairment has not been extensively discussed in the literature. The objective of this study is to develop a cost-effective and highly accessible machine learning model to predict the risk of cognitive impairment for up to five years before clinical insight. We utilized easily accessible data from the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) to train and evaluate various machine learning and deep learning models. A novel algorithm was developed to facilitate the selection of cost-effective models that offer high performance while minimizing development and operational costs. We conducted various assessments, including feature selection, time-series analyses, and external validation of the selected model. Our findings indicated that the Support Vector Machine (SVM) model was preferred over other high-performing neural network models because of its computational efficiency, achieving F2-scores of 0.828 in cross-validation and 0.750 in a generalizability test. Additionally, we found that demographic and historical health data are valuable for early prediction of cognitive impairment. This study demonstrates the potential of developing accessible solutions to predict cognitive impairment early using accurate and efficient machine learning models. Future interventions should consider creating cost-effective assessment tools to support global action plans and reduce the risk of cognitive impairment.

Establishing a machine learning dementia progression prediction model with multiple integrated data

OBJECTIVE

Dementia is a significant medical and social issue in most developed countries. Practical tools for predicting the progression of degenerative dementia are highly valuable. Machine learning (ML) methods facilitate the construction of effective models using real-world data, which may include missing values and various integrated datasets.

METHOD

This retrospective study analyzed data from 679 patients diagnosed with degenerative dementia at Fu Jen Catholic University Hospital, who were evaluated by neurologists, psychologists and followed for over two years. Predictive variables were categorized into demographic (D), clinical dementia rating (CDR), mini-mental state examination (MMSE), and laboratory data value (LV) groups. These categories were further integrated into three subgroups (D-CDR, D-CDR-MMSE, and D-CDR-MMSE-LV). We utilized the extreme gradient boosting (XGB) model to rank the importance of variables and identify the most effective feature combination via a step-wise approach.

RESULT

The D-CDR-MMSE-LV model combination showed robust performance with an excellent area under the receiver operating characteristic curve (AUC) and the highest sensitivity value (84.66). Employing both demographic and neuropsychiatric variables, our prediction model achieved an AUC of 83.74. By incorporating additional clinical information from laboratory data and applying our proposed feature selection strategy, we constructed a model based on eight variables that achieved an AUC of 85.12 using the XGB technique.

CONCLUSION

We established a machine-learning model to monitor the progression of dementia using a limited, real-world clinical dataset. The XGB technique identified eight critical variables across our integrated datasets, potentially providing clinicians with valuable guidance.

Multimodal Attention Network for Dementia Prediction

The early identification of an individual's dementia risk is crucial for disease prevention and the design of insurance products in an aging society. This study aims to accurately predict the future incidence risk of dementia in individuals by leveraging the advantages of neural networks. This is, however, complicated by the high dimensionality and sparsity of the International Classification of Diseases (ICD) codes when utilizing data from Taiwan's National Health Insurance, which includes individual profiles and medical records. Inspired by the click-through rate (CTR) problem in recommendation systems, where future user behavior is predicted based on their past consumption records, we address these challenges with a multimodal attention network for dementia (MAND), which incorporates an ICD code embedding layer and multihead self-attention to encode ICD codes and capture interactions among diseases. Additionally, we investigate the applicability of several CTR methods to the dementia prediction problem. MAND achieves an AUC of 0.9010, surpassing traditional CTR models and demonstrating its effectiveness. The highly flexible pipelined design allows for module replacement to meet specific requirements. Furthermore, the analysis of attention scores reveals diseases highly correlated with dementia, aligning with prior research and emphasizing the interpretability of the model. This research deepens our understanding of the diseases associated with dementia, and the accurate prediction provided can serve as an early warning for dementia occurrence, aiding in its prevention.

Predicting the onset of Alzheimer's disease and related dementia using electronic health records: findings from the cache county study on memory in aging (1995-2008)

INTRODUCTION

Clinical notes, biomarkers, and neuroimaging have proven valuable in dementia prediction models. Whether commonly available structured clinical data can predict dementia is an emerging area of research. We aimed to predict gold-standard, research-based diagnoses of dementia including Alzheimer's disease (AD) and/or Alzheimer's disease related dementias (ADRD), in addition to ICD-based AD and/or ADRD diagnoses, in a well-phenotyped, population-based cohort using a machine learning approach.

METHODS

Administrative healthcare data (k = 163 diagnostic features), in addition to census/vital record sociodemographic data (k = 6 features), were linked to the Cache County Study (CCS, 1995-2008).

RESULTS

Among successfully linked UPDB-CCS participants (n = 4206), 522 (12.4%) had incident dementia (AD alone, AD comorbid with ADRD, or ADRD alone) as per the CCS "gold standard" assessments. Random Forest models, with a 1-year prediction window, achieved the best performance with an Area Under the Curve (AUC) of 0.67. Accuracy declined for dementia subtypes: AD/ADRD (AUC = 0.65); ADRD (AUC = 0.49). Accuracy improved when using ICD-based dementia diagnoses (AUC = 0.77).

DISCUSSION

Commonly available structured clinical data (without labs, notes, or prescription information) demonstrate modest ability to predict "gold-standard" research-based AD/ADRD diagnoses, corroborated by prior research. Using ICD diagnostic codes to identify dementia as done in the majority of machine learning dementia prediction models, as compared to "gold-standard" dementia diagnoses, can result in higher accuracy, but whether these models are predicting true dementia warrants further research.

An intelligent learning system based on electronic health records for unbiased stroke prediction

Stroke has a negative impact on people's lives and is one of the leading causes of death and disability worldwide. Early detection of symptoms can significantly help predict stroke and promote a healthy lifestyle. Researchers have developed several methods to predict strokes using machine learning (ML) techniques. However, the proposed systems have suffered from the following two main problems. The first problem is that the machine learning models are biased due to the uneven distribution of classes in the dataset. Recent research has not adequately addressed this problem, and no preventive measures have been taken. Synthetic Minority Oversampling (SMOTE) has been used to remove bias and balance the training of the proposed ML model. The second problem is to solve the problem of lower classification accuracy of machine learning models. We proposed a learning system that combines an autoencoder with a linear discriminant analysis (LDA) model to increase the accuracy of the proposed ML model for stroke prediction. Relevant features are extracted from the feature space using the autoencoder, and the extracted subset is then fed into the LDA model for stroke classification. The hyperparameters of the LDA model are found using a grid search strategy. However, the conventional accuracy metric does not truly reflect the performance of ML models. Therefore, we employed several evaluation metrics to validate the efficiency of the proposed model. Consequently, we evaluated the proposed model's accuracy, sensitivity, specificity, area under the curve (AUC), and receiver operator characteristic (ROC). The experimental results show that the proposed model achieves a sensitivity and specificity of 98.51% and 97.56%, respectively, with an accuracy of 99.24% and a balanced accuracy of 98.00%.

Predicting Alzheimer's disease from cognitive footprints in mid and late life: How much can register data and machine learning help?

BACKGROUND

Real-world data with decades-long medical records are increasingly available alongside the growing adoption of machine learning in healthcare research. We evaluated the performance of machine learning models in predicting the risk of Alzheimer's disease (AD) using data from the Finnish national registers.

METHODS

We conducted a case-control study using data from the Finnish MEDALZ (Medication use and Alzheimer's disease) study. Altogether 56,741 individuals with incident AD diagnosis (age ≥ 65 years at diagnosis and born after 1922) and their 1:1 age-, sex-, and region of residence-matched controls were included. The association of risk factors, evaluated at different age periods (45-54, 55-64, 65+), and AD were assessed with logistic regression. Predictive accuracies of logistic regressions were compared with seven machine learning models (L1-regularized logistic regression, Naive bayes, Decision tree, Random Forest, Multilayer perceptron, XGBoost, and LightGBM).

FINDINGS

63.5 % of cases and controls were females and the mean age was 79.1 (SD = 5.1). The strongest associations with AD were observed for head injuries at age 55-64 (OR, 95 % CI 1.33, 1.19-1.48) and 65+ (1.31, 1.23-1.40), followed by antidepressant use (1.30, 1.22-1.38) at 55-64 and antipsychotic use (1.27, 1.19-1.35) at 65+. The predictive accuracies of all models were low, with the best performance (AUC 0.603) observed in Random Forest for predicting AD onset at age 65-69.

INTERPRETATION

Although significant associations were identified between many risk factors and AD, the low predictive accuracies suggest that specialised healthcare diagnosis data is not sufficient for predicting AD and linkage with other data sources is needed.

Machine and deep learning algorithms for classifying different types of dementia: A literature review

The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer's Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It's important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.

Predicting the onset of Alzheimer's disease and related dementia using Electronic Health Records: Findings from the Cache County Study on Memory in Aging (1995-2008)

Introduction

Clinical notes, biomarkers, and neuroimaging have been proven valuable in dementia prediction models. Whether commonly available structured clinical data can predict dementia is an emerging area of research. We aimed to predict Alzheimer's disease (AD) and Alzheimer's disease related dementias (ADRD) in a well-phenotyped, population-based cohort using a machine learning approach.

Methods

Administrative healthcare data (k=163 diagnostic features), in addition to Census/vital record sociodemographic data (k = 6 features), were linked to the Cache County Study (CCS, 1995-2008).

Results

Among successfully linked UPDB-CCS participants (n=4206), 522 (12.4%) had incident AD/ADRD as per the CCS "gold standard" assessments. Random Forest models, with a 1-year prediction window, achieved the best performance with an Area Under the Curve (AUC) of 0.67. Accuracy declined for dementia subtypes: AD/ADRD (AUC = 0.65); ADRD (AUC = 0.49).

DISCUSSION

Commonly available structured clinical data (without labs, notes, or prescription information) demonstrate modest ability to predict AD/ADRD, corroborated by prior research.

Machine Learning Accelerates De Novo Design of Antimicrobial Peptides

Efficient and precise design of antimicrobial peptides (AMPs) is of great importance in the field of AMP development. Computing provides opportunities for peptide de novo design. In the present investigation, a new machine learning-based AMP prediction model, AP_Sin, was trained using 1160 AMP sequences and 1160 non-AMP sequences. The results showed that AP_Sin correctly classified 94.61% of AMPs on a comprehensive dataset, outperforming the mainstream and open-source models (Antimicrobial Peptide Scanner vr.2, iAMPpred and AMPlify) and being effective in identifying AMPs. In addition, a peptide sequence generator, AP_Gen, was devised based on the concept of recombining dominant amino acids and dipeptide compositions. After inputting the parameters of the 71 tridecapeptides from antimicrobial peptides database (APD3) into AP_Gen, a tridecapeptide bank consisting of de novo designed 17,496 tridecapeptide sequences were randomly generated, from which 2675 candidate AMP sequences were identified by AP_Sin. Chemical synthesis was performed on 180 randomly selected candidate AMP sequences, of which 18 showed high antimicrobial activities against a wide range of the tested pathogenic microorganisms, and 16 of which had a minimal inhibitory concentration of less than 10 μg/mL against at least one of the tested pathogenic microorganisms. The method established in this research accelerates the discovery of valuable candidate AMPs and provides a novel approach for de novo design of antimicrobial peptides.

Deep learning approaches for seizure video analysis: A review

Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied or not by other observable features such as altered facial expressions. The analysis of these clinical signs, referred to as semiology, is subject to observer variations when specialists evaluate video-recorded events in the clinical setting. To enhance the accuracy and consistency of evaluations, computer-aided video analysis of seizures has emerged as a natural avenue. In the field of medical applications, deep learning and computer vision approaches have driven substantial advancements. Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting. While vision-based technologies do not aim to replace clinical expertise, they can significantly contribute to medical decision-making and patient care by providing quantitative evidence and decision support. Behavior monitoring tools offer several advantages such as providing objective information, detecting challenging-to-observe events, reducing documentation efforts, and extending assessment capabilities to areas with limited expertise. The main applications of these could be (1) improved seizure detection methods; (2) refined semiology analysis for predicting seizure type and cerebral localization. In this paper, we detail the foundation technologies used in vision-based systems in the analysis of seizure videos, highlighting their success in semiology detection and analysis, focusing on work published in the last 7 years. We systematically present these methods and indicate how the adoption of deep learning for the analysis of video recordings of seizures could be approached. Additionally, we illustrate how existing technologies can be interconnected through an integrated system for video-based semiology analysis. Each module can be customized and improved by adapting more accurate and robust deep learning approaches as these evolve. Finally, we discuss challenges and research directions for future studies.

Deep learning applications in vascular dementia using neuroimaging

PURPOSE OF REVIEW

Vascular dementia (VaD) is the second common cause of dementia after Alzheimer's disease, and deep learning has emerged as a critical tool in dementia research. The aim of this article is to highlight the current deep learning applications in VaD-related imaging biomarkers and diagnosis.

RECENT FINDINGS

The main deep learning technology applied in VaD using neuroimaging data is convolutional neural networks (CNN). CNN models have been widely used for lesion detection and segmentation, such as white matter hyperintensities (WMH), cerebral microbleeds (CMBs), perivascular spaces (PVS), lacunes, cortical superficial siderosis, and brain atrophy. Applications in VaD subtypes classification also showed excellent results. CNN-based deep learning models have potential for further diagnosis and prognosis of VaD.

SUMMARY

Deep learning neural networks with neuroimaging data in VaD research represent significant promise for advancing early diagnosis and treatment strategies. Ongoing research and collaboration between clinicians, data scientists, and neuroimaging experts are essential to address challenges and unlock the full potential of deep learning in VaD diagnosis and management.

Comparative study of machine learning methods for modeling associations between risk factors and future dementia cases

A substantial portion of dementia risk can be attributed to modifiable risk factors that can be affected by lifestyle changes. Identifying the contributors to dementia risk could prove valuable. Recently, machine learning methods have been increasingly applied to healthcare data. Several studies have attempted to predict dementia progression by using such techniques. This study aimed to compare the performance of different machine-learning methods in modeling associations between known cognitive risk factors and future dementia cases. A subset of the AGES-Reykjavik Study dataset was analyzed using three machine-learning methods: logistic regression, random forest, and neural networks. Data were collected twice, approximately five years apart. The dataset included information from 1,491 older adults who underwent a cognitive screening process and were considered to have healthy cognition at baseline. Cognitive risk factors included in the models were based on demographics, MRI data, and other health-related data. At follow-up, participants were re-evaluated for dementia using the same cognitive screening process. Various performance metrics for all three machine learning algorithms were assessed. The study results indicate that a random forest algorithm performed better than neural networks and logistic regression in predicting the association between cognitive risk factors and dementia. Compared to more traditional statistical analyses, machine-learning methods have the potential to provide more accurate predictions about which individuals are more likely to develop dementia than others.

Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia

Introduction: Dementia is a condition (a collection of related signs and symptoms) that causes a continuing deterioration in cognitive function, and millions of people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining dementia rely primarily on clinical examinations, analyzing medical records, and administering cognitive and neuropsychological testing. However, these methods are time-consuming and costly in terms of treatment. Therefore, this study aims to present a noninvasive method for the early prediction of dementia so that preventive steps should be taken to avoid dementia. Methods: We developed a hybrid diagnostic system based on statistical and machine learning (ML) methods that used patient electronic health records to predict dementia. The dataset used for this study was obtained from the Swedish National Study on Aging and Care (SNAC), with a sample size of 43040 and 75 features. The newly constructed diagnostic extracts a subset of useful features from the dataset through a statistical method (F-score). For the classification, we developed an ensemble voting classifier based on five different ML models: decision tree (DT), naive Bayes (NB), logistic regression (LR), support vector machines (SVM), and random forest (RF). To address the problem of ML model overfitting, we used a cross-validation approach to evaluate the performance of the proposed diagnostic system. Various assessment measures, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and Matthew's correlation coefficient (MCC), were used to thoroughly validate the devised diagnostic system's efficiency. Results: According to the experimental results, the proposed diagnostic method achieved the best accuracy of 98.25%, as well as sensitivity of 97.44%, specificity of 95.744%, and MCC of 0.7535. Discussion: The effectiveness of the proposed diagnostic approach is compared to various cutting-edge feature selection techniques and baseline ML models. From experimental results, it is evident that the proposed diagnostic system outperformed the prior feature selection strategies and baseline ML models regarding accuracy.

Comparison of multiple linear regression and machine learning methods in predicting cognitive function in older Chinese type 2 diabetes patients

INTRODUCTION

The prevalence of type 2 diabetes (T2D) has increased dramatically in recent decades, and there are increasing indications that dementia is related to T2D. Previous attempts to analyze such relationships principally relied on traditional multiple linear regression (MLR). However, recently developed machine learning methods (Mach-L) outperform MLR in capturing non-linear relationships. The present study applied four different Mach-L methods to analyze the relationships between risk factors and cognitive function in older T2D patients, seeking to compare the accuracy between MLR and Mach-L in predicting cognitive function and to rank the importance of risks factors for impaired cognitive function in T2D.

METHODS

We recruited older T2D between 60-95 years old without other major comorbidities. Demographic factors and biochemistry data were used as independent variables and cognitive function assessment (CFA) was conducted using the Montreal Cognitive Assessment as an independent variable. In addition to traditional MLR, we applied random forest (RF), stochastic gradient boosting (SGB), Naïve Byer's classifier (NB) and eXtreme gradient boosting (XGBoost).

RESULTS

Totally, the test cohort consisted of 197 T2D (98 men and 99 women). Results showed that all ML methods outperformed MLR, with symmetric mean absolute percentage errors for MLR, RF, SGB, NB and XGBoost respectively of 0.61, 0.599, 0.606, 0.599 and 0.2139. Education level, age, frailty score, fasting plasma glucose and body mass index were identified as key factors in descending order of importance.

CONCLUSION

In conclusion, our study demonstrated that RF, SGB, NB and XGBoost are more accurate than MLR for predicting CFA score, and identify education level, age, frailty score, fasting plasma glucose, body fat and body mass index as important risk factors in an older Chinese T2D cohort.

Rethinking Dementia Risk Prediction: A Critical Evaluation of a Multimodal Machine Learning Predictive Model

A recent study by Ding et al. explores the integration of artificial intelligence (AI) in predicting dementia risk over a 10-year period using a multimodal approach. While revealing the potential of machine learning models in identifying high-risk individuals through neuropsychological testing, MRI imaging, and clinical risk factors, the imperative of dynamic frailty assessment emerges for accurate late-life dementia prediction. The commentary highlights challenges associated with AI models, including dimensionality and data standardization, emphasizing the critical need for a dynamic, comprehensive approach to reflect the evolving nature of dementia and improve predictive accuracy.

Brain Disorder Detection and Diagnosis using Machine Learning and Deep Learning - A Bibliometric Analysis

BACKGROUND AND OBJECTIVE

Brain disorders are one of the major global mortality issues, and their early detection is crucial for healing. Machine learning, specifically deep learning, is a technology that is increasingly being used to detect and diagnose brain disorders. Our objective is to provide a quantitative bibliometric analysis of the field to inform researchers about trends that can inform their Research directions in the future.

METHODS

We carried out a bibliometric analysis to create an overview of brain disorder detection and diagnosis using machine learning and deep learning. Our bibliometric analysis includes 1550 articles gathered from the Scopus database on automated brain disorder detection and diagnosis using machine learning and deep learning published from 2015 to May 2023. A thorough bibliometric análisis is carried out with the help of Biblioshiny and the VOSviewer platform. Citation analysis and various measures of collaboration are analyzed in the study.

RESULTS

According to a study, maximum research is reported in 2022, with a consistent rise from preceding years. The majority of the authors referenced have concentrated on multiclass classification and innovative convolutional neural network models that are effective in this field. A keyword analysis revealed that among the several brain disorder types, Alzheimer's, autism, and Parkinson's disease had received the greatest attention. In terms of both authors and institutes, the USA, China, and India are among the most collaborating countries. We built a future research agenda based on our findings to help progress research on machine learning and deep learning for brain disorder detection and diagnosis.

CONCLUSION

In summary, our quantitative bibliometric analysis provides useful insights about trends in the field and points them to potential directions in applying machine learning and deep learning for brain disorder detection and diagnosis.

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New Horizons in artificial intelligence in the healthcare of older people

Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access.

Artificial intelligence for dementia research methods optimization

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

Dementia prediction in the general population using clinically accessible variables: a proof-of-concept study using machine learning. The AGES-Reykjavik study

BACKGROUND

Early identification of dementia is crucial for prompt intervention for high-risk individuals in the general population. External validation studies on prognostic models for dementia have highlighted the need for updated models. The use of machine learning in dementia prediction is in its infancy and may improve predictive performance. The current study aimed to explore the difference in performance of machine learning algorithms compared to traditional statistical techniques, such as logistic and Cox regression, for prediction of all-cause dementia. Our secondary aim was to assess the feasibility of only using clinically accessible predictors rather than MRI predictors.

METHODS

Data are from 4,793 participants in the population-based AGES-Reykjavik Study without dementia or mild cognitive impairment at baseline (mean age: 76 years, % female: 59%). Cognitive, biometric, and MRI assessments (total: 59 variables) were collected at baseline, with follow-up of incident dementia diagnoses for a maximum of 12 years. Machine learning algorithms included elastic net regression, random forest, support vector machine, and elastic net Cox regression. Traditional statistical methods for comparison were logistic and Cox regression. Model 1 was fit using all variables and model 2 was after feature selection using the Boruta package. A third model explored performance when leaving out neuroimaging markers (clinically accessible model). Ten-fold cross-validation, repeated ten times, was implemented during training. Upsampling was used to account for imbalanced data. Tuning parameters were optimized for recalibration automatically using the caret package in R.

RESULTS

19% of participants developed all-cause dementia. Machine learning algorithms were comparable in performance to logistic regression in all three models. However, a slight added performance was observed in the elastic net Cox regression in the third model (c = 0.78, 95% CI: 0.78-0.78) compared to the traditional Cox regression (c = 0.75, 95% CI: 0.74-0.77).

CONCLUSIONS

Supervised machine learning only showed added benefit when using survival techniques. Removing MRI markers did not significantly worsen our model's performance. Further, we presented the use of a nomogram using machine learning methods, showing transportability for the use of machine learning models in clinical practice. External validation is needed to assess the use of this model in other populations. Identifying high-risk individuals will amplify prevention efforts and selection for clinical trials.

Sooty Tern Optimization Algorithm-Based Deep Learning Model for Diagnosing NSCLC Tumours

Lung cancer is one of the most common causes of cancer deaths in the modern world. Screening of lung nodules is essential for early recognition to facilitate treatment that improves the rate of patient rehabilitation. An increase in accuracy during lung cancer detection is vital for sustaining the rate of patient persistence, even though several research works have been conducted in this research domain. Moreover, the classical system fails to segment cancer cells of different sizes accurately and with excellent reliability. This paper proposes a sooty tern optimization algorithm-based deep learning (DL) model for diagnosing non-small cell lung cancer (NSCLC) tumours with increased accuracy. We discuss various algorithms for diagnosing models that adopt the Otsu segmentation method to perfectly isolate the lung nodules. Then, the sooty tern optimization algorithm (SHOA) is adopted for partitioning the cancer nodules by defining the best characteristics, which aids in improving diagnostic accuracy. It further utilizes a local binary pattern (LBP) for determining appropriate feature retrieval from the lung nodules. In addition, it adopts CNN and GRU-based classifiers for identifying whether the lung nodules are malignant or non-malignant depending on the features retrieved during the diagnosing process. The experimental results of this SHOA-optimized DNN model achieved an accuracy of 98.32%, better than the baseline schemes used for comparison.

Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification

Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using dementia symptoms. However, these methods have fundamental limitations, such as low accuracy and bias in machine learning (ML) models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive synthetic sampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extraction techniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM) using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM are calibrated by employing the grid search approach. It is evident from the experimental results that the newly pr oposed model (FEB-SVM) improves the dementia prediction accuracy of the conventional SVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testing accuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of 86.59, F1-score of 89.12%, and Matthew's correlation coefficient (MCC) of 0.4987. Moreover, the newly proposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.

Predicting Progression to Clinical Alzheimer's Disease Dementia Using the Random Survival Forest

BACKGROUND

Assessing the risk of developing clinical Alzheimer's disease (AD) dementia, by machine learning survival analysis approaches, among participants registered in Alzheimer's Disease Centers is important for AD dementia management.

OBJECTIVE

To construct a prediction model for the onset time of clinical AD dementia using the National Alzheimer Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) registered cohorts.

METHODS

A model was constructed using the Random Survival Forest (RSF) approach and internally and externally validated on the NACC cohort and the ADNI cohort. An R package and a Shiny app were provided for accessing the model.

RESULTS

We built a predictive model having the six predictors: delayed logical memory score (story recall), CDR® Dementia Staging Instrument - Sum of Boxes, general orientation in CDR®, ability to remember dates and ability to pay bills in the Functional Activities Questionnaire, and patient age. The C indices of the model were 90.82% (SE = 0.71%) and 86.51% (SE = 0.75%) in NACC and ADNI respectively. The time-dependent AUC and accuracy at 48 months were 92.48% (SE = 1.12%) and 88.66% (SE = 1.00%) respectively in NACC, and 90.16% (SE = 1.12%) and 85.00% (SE = 1.14%) respectively in ADNI.

CONCLUSION

The model showed good prediction performance and the six predictors were easy to obtain, cost-effective, and non-invasive. The model could be used to inform clinicians and patients on the probability of developing clinical AD dementia in 4 years with high accuracy.