An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Integrating Social Determinants of Health and Established Risk Factors to Predict Cardiovascular Disease Risk Among Healthy Older Adults

BACKGROUND

Recent evidence underscores the significant impact of social determinants of health (SDoH) on cardiovascular disease (CVD). However, available CVD risk assessment tools often neglect SDoH. This study aimed to integrate SDoH with traditional risk factors to predict CVD risk.

METHODS

The data was sourced from the ASPirin in Reducing Events in the Elderly (ASPREE) longitudinal study, and its sub-study, the ASPREE Longitudinal Study of Older Persons (ALSOP). The study included 12,896 people (5884 men and 7012 women) aged 70 or older who were initially free of CVD, dementia, and independence-limiting physical disability. The participants were followed for a median of eight years. CVD risk was predicted using state-of-the-art machine learning (ML) and deep learning (DL) models: Random Survival Forest (RSF), Deepsurv, and Neural Multi-Task Logistic Regression (NMTLR), incorporating both SDoH and traditional CVD risk factors as candidate predictors. The permutation-based feature importance method was further utilized to assess the predictive potential of the candidate predictors.

RESULTS

Among men, the RSF model achieved relatively good performance (C-index = 0.732, integrated brier score (IBS) = 0.071, 5-year and 10-year AUC = 0.657 and 0.676 respectively). For women, DeepSurv was the best-performing model (C-index = 0.670, IBS = 0.042, 5-year and 10-year AUC = 0.676 and 0.677 respectively). Regarding the contribution of the candidate predictors, for men, age, urine albumin-to-creatinine ratio, and smoking, along with SDoH variables, were identified as the most significant predictors of CVD. For women, SDoH variables, such as social network, living arrangement, and education, predicted CVD risk better than the traditional risk factors, with age being the exception.

CONCLUSION

SDoH can improve the accuracy of CVD risk prediction and emerge among the main predictors for CVD. The influence of SDoH was greater for women than for men, reflecting gender-specific impacts of SDoH.

Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models

INTRODUCTION

Low back pain (LBP) is a significant global health burden, with variable treatment outcomes and an unclear underlying molecular mechanism. Effective prediction of treatment responses remains a challenge. In this study, we aimed to develop gene signature-based machine learning models using transcriptomic data from peripheral immune cells to predict treatment outcomes in patients with LBP.

METHODS

The transcriptomic data of patients with LBP from peripheral immune cells were retrieved from the GEO database. Patients with LBP were recruited, and treatment outcomes were assessed after 3 months. Patients were classified into two groups: those with resolved pain and those with persistent pain. Differentially expressed genes (DEGs) between the two groups were identified through bioinformatic analysis. Key genes were selected using five machine learning models, including Lasso, Elastic Net, Random Forest, SVM, and GBM. These key genes were then used to train 45 machine learning models by combining nine different algorithms: Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting Machine, Multilayer Perceptron, Naive Bayes, and Linear Discriminant Analysis. Five-fold cross-validation was employed to ensure robust model evaluation and minimize overfitting. In each fold, the dataset was split into training and validation sets, with model performance assessed using multiple metrics including accuracy, precision, recall, and F1 score. The final model performance was reported as the mean and standard deviation across all five folds, providing a more reliable estimate of the models' ability to predict LBP treatment outcomes using gene expression data from peripheral immune cells.

RESULTS

A total of 61 DEGs were identified between patients with resolved and persistent pain. From these genes, 45 machine learning models were constructed using different combinations of feature selection methods and classification algorithms. The Elastic Net with Logistic Regression achieved the highest accuracy of 88.7% ± 8.0% (mean ± standard deviation), followed closely by Elastic Net with Linear Discriminant Analysis (88.7% ± 7.5%) and Lasso with Multilayer Perceptron (87.7% ± 6.7%). Overall, 15 models demonstrated robust performance with accuracy > 80%, suggesting the reliability of our machine learning approach in predicting LBP treatment outcomes. The SHapley Additive exPlanations (SHAP) method was used to visualize the contribution of core genes to model performance, highlighting their roles in predicting treatment outcomes.

CONCLUSION

The study demonstrates the potential of using transcriptomic data from peripheral immune cells and machine learning models to predict treatment outcomes in patients with LBP. The identification of key genes and the high accuracy of certain models provide a basis for future personalized treatment strategies in LBP management. Visualizing gene importance with SHAP adds interpretability to the predictive models, enhancing their clinical relevance.

Development of risk models for early detection and prediction of chronic kidney disease in clinical settings

Chronic kidney disease (CKD) imposes a high burden with high mortality and morbidity rates. Early detection of CKD is imperative in preventing the adverse outcomes attributed to the later stages. Therefore, this study aims to utilize machine learning techniques to predict CKD at early stages. This study uses data obtained from a large longitudinal cohort study. The features include patients' sociodemographic, anthropometric, and laboratory tests that are mostly associated with CKD based on national and international studies. Missing data and outliers were deleted using listwise and interquartile range techniques, respectively. Data initially remained imbalanced to investigate the ability of models to work on imbalanced datasets. Stratified K-folds cross-validation, a robust approach that performs well on imbalanced data, was further performed to enhance the splitting. Interestingly, an interaction was found between age and gender where contrasting data was generated, therefore, to avoid this interaction gender-specific algorithms were developed. Four main algorithms and four algorithms using the stratified K-folds cross-validation technique, consisting of gender-specific Random Forest and feedforward Neural Networks were developed using the preprocessed data of 6855 participants. The RF model in women exhibited the highest AUC of 0.90 followed closely by 0.89 in their NN model. Both models constructed for men yielded an AUC of 0.88. Sensitivity scores were higher in men compared to women. Models demonstrated subpar results regarding specificity, however, the high precision and F1 scores, make the models extremely valuable in a clinical setting to accurately identify CKD cases while minimizing false positive diagnoses. Moreover, the results from stratified K-fold cross-validation indicated that the NN models were more sensitive to the imbalanced dataset and demonstrated a marked increase in performance, particularly specificity, after this approach. These data offer valuable insights for the development of future risk stratification models for CKD.

ChatGPT-Assisted Machine Learning for Chronic Disease Classification and Prediction: A Developmental and Validation Study

Background Chronic diseases such as chronic kidney disease (CKD), chronic liver disease (CLD), tuberculosis (TB), dementia, and heart disease are global health concerns of significant importance, representing major causes of morbidity and mortality worldwide. Early diagnosis and interventions are critical to improve patient outcomes and reduce healthcare costs. Methods This prospective observational study analyzed clinical data from 270 patients (calculated using G*Power 3.1.9.7 analysis (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany), α = 0.05, power = 0.80), with 260 (96.3%) completing the protocol. The cohort comprised 149 (55.2%) males and 121 (44.8%) females, distributed across CKD (n=55, 21.2%), CLD (n=52, 20.0%), TB (n=51, 19.6%), dementia (n=50, 19.2%), and heart disease (n=52, 20.0%). Three ML models were employed with ChatGPT version 3.5 assistance (OpenAI, San Francisco, CA, USA) in feature selection and hyperparameter optimization: logistic regression, random forest, and support vector machines. Model performance was evaluated using accuracy, sensitivity, specificity, precision, recall, F1-score, and AUC-ROC metrics. Ten-fold cross-validation was applied to ensure robustness. Results The random forest model demonstrated superior performance, achieving the highest accuracy in predicting CKD (47/55, 85.3%, p < 0.001, sensitivity 45/55, 82.5%, specificity 48/55, 87.2%) and heart disease (46/52, 88.2%, p < 0.001, sensitivity 45/52, 85.7%, specificity 47/52, 90.1%). Logistic regression effectively predicted TB (41/51, 80.1%, p < 0.01) and dementia (41/50, 82.4%, p < 0.01). Key predictive parameters included hemoglobin (median 10.2 g/dL, IQR 8.4-12.6) and erythrocyte sedimentation rate (median 42.0 mm/hr, IQR 20.0-65.0). Model validation showed high consistency, with positive acid-fast bacilli in 40/51 (78.4%) TB cases and characteristic radiological findings in 43/51 (84.3%) cases. Conclusion ML algorithms, particularly random forest, show promising potential in chronic disease classification and prediction. The integration of ChatGPT enhanced model development through optimized feature selection and hyperparameter tuning. Future research should focus on external validation through multi-center studies and prospective clinical trials.

Comorbidity progression analysis: patient stratification and comorbidity prediction using temporal comorbidity network

Objective

The study aims to identify distinct population-specific comorbidity progression patterns, timely detect potential comorbidities, and gain better understanding of the progression of comorbid conditions among patients.

Methods

This work presents a comorbidity progression analysis framework that utilizes temporal comorbidity networks (TCN) for patient stratification and comorbidity prediction. We propose a TCN construction approach that utilizes longitudinal, temporal diagnosis data of patients to construct their TCN. Subsequently, we employ the TCN for patient stratification by conducting preliminary analysis, and typical prescription analysis to uncover potential comorbidity progression patterns in different patient groups. Finally, we propose an innovative comorbidity prediction method by utilizing the distance-matched temporal comorbidity network (TCN-DM). This method identifies similar patients with disease prevalence and disease transition patterns and combines their diagnosis information with that of the current patient to predict potential comorbidity at the patient's next visit.

Results

This study validated the capability of the framework using a real-world dataset MIMIC-III, with heart failure (HF) as interested disease to investigate comorbidity progression in HF patients. With TCN, this study can identify four significant distinctive HF subgroups, revealing the progression of comorbidities in patients. Furthermore, compared to other methods, TCN-DM demonstrated better predictive performance with F1-Score values ranging from 0.454 to 0.612, showcasing its superiority.

Conclusions

This study can identify comorbidity patterns for individuals and population, and offer promising prediction for future comorbidity developments in patients.

Health Risk Assessment Using Machine Learning: Systematic Review

According to the World Health Organization, chronic illnesses account for over 70% of deaths globally, underscoring the need for effective health risk assessment (HRA). While machine learning (ML) has shown potential in enhancing HRA, no systematic review has explored its application in general health risk assessments. Existing reviews typically focus on specific conditions. This paper reviews published articles that utilize ML for HRA, and it aims to identify the model development methods. A systematic review following Tranfield et al.’s three-stage approach was conducted, and it adhered to the PRISMA protocol. The literature was sourced from five databases, including PubMed. Of the included articles, 42% (11/26) addressed general health risks. Secondary data sources were most common (14/26, 53.85%), while primary data were used in eleven studies, with nine (81.81%) using data from a specific population. Random forest was the most popular algorithm, which was used in nine studies (34.62%). Notably, twelve studies implemented multiple algorithms, while seven studies incorporated model interpretability techniques. Although these studies have shown promise in addressing digital health inequities, more research is needed to include diverse sample populations, particularly from underserved communities, to enhance the generalizability of existing models. Furthermore, model interpretability should be prioritized to ensure transparent, trustworthy, and broadly applicable healthcare solutions.

Explainability of artificial neural network in predicting career fulfilment among medical doctors in developing nations: Applicability and implications

BACKGROUND

Career fulfilment among medical doctors is crucial for job satisfaction, retention, and healthcare quality, especially in developing nations with challenging healthcare systems. Traditional career guidance methods struggle to address the complexities of career fulfilment. While recent advancements in machine learning, particularly Artificial Neural Network (ANN) models, offer promising solutions for personalized career predictions, their applicability, interpretability, and impact remain challenging.

METHOD

This study explores the applicability, explainability, and implications of ANN models in predicting career fulfillment among medical doctors in developing nations, considering socio-economic, psychological, and professional factors. Box plots visualized data distribution, while Heatmaps assessed data intensity and relationships. Matthew's correlation coefficient and Taylor's chart were used to evaluate model performance. Input feature contributions to ANN predictions were analyzed using permutation importance, SHAP, LIME, and Williams plots. The model was tested on a dataset tailored to medical professionals in Nigeria and China, with evaluation metrics including Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R2 Score.

RESULTS

The ANN model demonstrates strong predictive accuracy, capturing relationships between input factors and outcomes. For Chinese doctors, it achieved an MSE of 0.0004 and R2 of 0.9994, while for Nigerian doctors, it recorded an MSE of 0.0003 and R2 of 0.9998. Key factors for Chinese doctors' satisfaction were IF1 and IF2, while EF1 and EF3 were crucial in preventing dissatisfaction. For Nigerian doctors, IF2 and IF3 drove satisfaction, while EF1 and EF4 were significant in avoiding dissatisfaction.

CONCLUSION

The results highlights the ANN model's effectiveness in predicting career fulfillment among medical doctors in developing nations, offering a valuable tool for career guidance, policymaking, and improving job satisfaction, retention, and healthcare quality.

Health Equity and Ethical Considerations in Using Artificial Intelligence in Public Health and Medicine

This commentary explores the critical roles of health equity and ethical considerations in the deployment of artificial intelligence (AI) in public health and medicine. As AI increasingly permeates these fields, it promises substantial benefits but also poses risks that could exacerbate existing disparities and ethical challenges. This commentary delves into the current integration of AI technologies, underscores the importance of ethical social responsibility, and discusses the implications for practice and policy. Recommendations are provided to ensure AI advancements are leveraged responsibly, promoting equitable health outcomes and adhering to rigorous ethical standards across all populations.

Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics

Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson's disease. Sensor data collected from wearable gyroscopes located at the sole of the patient's shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson's as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen's Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.

Critical review of self-diagnosis of mental health conditions using artificial intelligence

The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI-driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well-being within the confines of their homes. Nonetheless, the widespread trend of self-diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self-diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI-driven self-diagnosis.

Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence-Based Prognosis for Patients With Advanced Solid Tumors

PURPOSE

Use of artificial intelligence (AI) in cancer care is increasing. What remains unclear is how best to design patient-facing systems that communicate AI output. With oncologist input, we designed an interface that presents patient-specific, machine learning-based 6-month survival prognosis information designed to aid oncology providers in preparing for and discussing prognosis with patients with advanced solid tumors and their caregivers. The primary purpose of this study was to assess patient and caregiver perceptions and identify enhancements of the interface for communicating 6-month survival and other prognosis information when making treatment decisions concerning anticancer and supportive therapy.

METHODS

This qualitative study included interviews and focus groups conducted between November and December 2022. Purposive sampling was used to recruit former patients with cancer and/or former caregivers of patients with cancer who had participated in cancer treatment decisions from Utah or elsewhere in the United States. Categories and themes related to perceptions of the interface were identified.

RESULTS

We received feedback from 20 participants during eight individual interviews and two focus groups, including four cancer survivors, 13 caregivers, and three representing both. Overall, most participants expressed positive perceptions about the tool and identified its value for supporting decision making, feeling less alone, and supporting communication among oncologists, patients, and their caregivers. Participants identified areas for improvement and implementation considerations, particularly that oncologists should share the tool and guide discussions about prognosis with patients who want to receive the information.

CONCLUSION

This study revealed important patient and caregiver perceptions of and enhancements for the proposed interface. Originally designed with input from oncology providers, patient and caregiver participants identified additional interface design recommendations and implementation considerations to support communication about prognosis.

Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models

Introduction

Decades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions.

Methods

Using the 2021 Behavioral Risk Factor Surveillance Survey, we developed several ML models-random forest, logistic regression, support vector machine, Naïve Bayes, and K-Nearest Neighbor-over data from a sample of 52,268 respondents. We predicted 13 chronic health conditions based on ACE history, health behaviors, SDoH, and demographics. We further assessed each variable's importance in outcome prediction for model interpretability. We evaluated model performance via the Area Under the Curve (AUC) score.

Results

With the inclusion of data on ACEs, our models outperformed or demonstrated similar accuracies to existing models in the literature that used SDoH to predict health outcomes. The most accurate models predicted diabetes, pulmonary diseases, and heart attacks. The random forest model was the most effective for diabetes (AUC = 0.784) and heart attacks (AUC = 0.732), and the logistic regression model most accurately predicted pulmonary diseases (AUC = 0.753). The strongest predictors across models were age, ever monitored blood sugar or blood pressure, count of the monitoring behaviors for blood sugar or blood pressure, BMI, time of last cholesterol check, employment status, income, count of vaccines received, health insurance status, and total ACEs. A cumulative measure of ACEs was a stronger predictor than individual ACEs.

Discussion

Our models can provide an interpretable, trauma-informed framework to identify and intervene with at-risk individuals early to prevent chronic health conditions and address their inequalities in the U.S.

Ensemble model for predicting chronic non-communicable diseases using Latin square extraction and fuzzy-artificial neural networks from 2013 to 2019

Background

The presented study tracks the increase or decrease in the prevalence of seventeen different chronic non-communicable diseases in Serbia. This analysis considers factors such as region, age, and gender and is based on data from two national cross-sectional studies conducted in 2013 and 2019. The research aims to accurately identify the regions with the highest percentage of affected individuals, as well as their respective age and gender groups. The ultimate goal is to facilitate organized, free preventive screenings for these population categories within a very short time-frame in the future.

Materials and methods

The study analyzed two cross-sectional studies conducted between 2013 and 2019, using data obtained from the Institute of Public Health of Serbia. Both studies involved a total of 27801 participants. The study compared the performance of Decision Tree and Support Vector Regressor models with artificial neural network (ANN) models that employed two encoding functions. The new methodology for the ANN-L36 model was based on artificial neural networks constructed using a Latin square (L36) design, incorporating Taguchi's robust design optimization.

Results

The results of the analysis from three different models have shown that cardiovascular diseases are the most prevalent illnesses among the population in Serbia, with hypertension as the leading condition in all regions, particularly among individuals aged 64 to 75 years, and more prevalent among females. In 2019, there was a decrease in the percentage of the leading disease, hypertension, compared to 2013, with a decrease from 34.0% to 32.2%. The ANN-L36 model with Fuzzy encoding function demonstrated the highest precision, achieving the smallest relative error of 0.1%.

Conclusion

To date, no studies have been conducted at the national level in Serbia to comprehensively track and identify chronic diseases in the manner proposed by this study. The model presented in this research will be implemented in practice and is set to significantly contribute to the future healthcare framework in Serbia, shaping and advancing the approach towards addressing these conditions. Furthermore, experimental evidence has shown that Taguchi's optimization approach yields the best results for identifying various chronic non-communicable diseases.

A review on the use of machine learning techniques in monkeypox disease prediction

Infectious diseases have posed a global threat recently, progressing from endemic to pandemic. Early detection and finding a better cure are methods for curbing the disease and its transmission. Machine learning (ML) has demonstrated to be an ideal approach for early disease diagnosis. This review highlights the use of ML algorithms for monkeypox (MP). Various models, such as CNN, DL, NLP, Naïve Bayes, GRA-TLA, HMD, ARIMA, SEL, Regression analysis, and Twitter posts were built to extract useful information from the dataset. These findings show that detection, classification, forecasting, and sentiment analysis are primarily analyzed. Furthermore, this review will assist researchers in understanding the latest implementations of ML in MP and further progress in the field to discover potent therapeutics.

Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population

It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.

A robust deep learning approach for tomato plant leaf disease localization and classification

Tomato plants' disease detection and classification at the earliest stage can save the farmers from expensive crop sprays and can assist in increasing the food quantity. Although, extensive work has been presented by the researcher for the tomato plant disease classification, however, the timely localization and identification of various tomato leaf diseases is a complex job as a consequence of the huge similarity among the healthy and affected portion of plant leaves. Furthermore, the low contrast information between the background and foreground of the suspected sample has further complicated the plant leaf disease detection process. To deal with the aforementioned challenges, we have presented a robust deep learning (DL)-based approach namely ResNet-34-based Faster-RCNN for tomato plant leaf disease classification. The proposed method includes three basic steps. Firstly, we generate the annotations of the suspected images to specify the region of interest (RoI). In the next step, we have introduced ResNet-34 along with Convolutional Block Attention Module (CBAM) as a feature extractor module of Faster-RCNN to extract the deep key points. Finally, the calculated features are utilized for the Faster-RCNN model training to locate and categorize the numerous tomato plant leaf anomalies. We tested the presented work on an accessible standard database, the PlantVillage Kaggle dataset. More specifically, we have obtained the mAP and accuracy values of 0.981, and 99.97% respectively along with the test time of 0.23 s. Both qualitative and quantitative results confirm that the presented solution is robust to the detection of plant leaf disease and can replace the manual systems. Moreover, the proposed method shows a low-cost solution to tomato leaf disease classification which is robust to several image transformations like the variations in the size, color, and orientation of the leaf diseased portion. Furthermore, the framework can locate the affected plant leaves under the occurrence of blurring, noise, chrominance, and brightness variations. We have confirmed through the reported results that our approach is robust to several tomato leaf diseases classification under the varying image capturing conditions. In the future, we plan to extend our approach to apply it to other parts of plants as well.

Transfer learning-based modified inception model for the diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative ailment, which gradually deteriorates memory and weakens the cognitive functions and capacities of the body, such as recall and logic. To diagnose this disease, CT, MRI, PET, etc. are used. However, these methods are time-consuming and sometimes yield inaccurate results. Thus, deep learning models are utilized, which are less time-consuming and yield results with better accuracy, and could be used with ease. This article proposes a transfer learning-based modified inception model with pre-processing methods of normalization and data addition. The proposed model achieved an accuracy of 94.92 and a sensitivity of 94.94. It is concluded from the results that the proposed model performs better than other state-of-the-art models. For training purposes, a Kaggle dataset was used comprising 6,200 images, with 896 mild demented (M.D) images, 64 moderate demented (Mod.D) images, and 3,200 non-demented (N.D) images, and 1,966 veritably mild demented (V.M.D) images. These models could be employed for developing clinically useful results that are suitable to descry announcements in MRI images.

Gene signature of m6A RNA regulators in diagnosis, prognosis, treatment, and immune microenvironment for cervical cancer

Continuing studies imply that m⁶A RNA modification is involved in the development of cervical cancer (CC), but lack strong support on recurrence and diagnosis prediction. In this research, a comprehensive analysis of 33 m⁶A regulators was performed to fulfill them. Here, we performed diagnostic and prognosis models and identified key regulators, respectively. Then the CC patients were separated into two clusters in accordance with 33 regulators, and participants in the cluster 1 had a worse prognosis. Subsequently, the m⁶AScore was calculated to quantify the m⁶A modification pattern based on regulators and we found that patients in cluster 1 had higher m⁶AScore. Afterwards, immune microenvironment, cell infiltration, escape analyses and tumor burden mutation analyses were executed, and results showed that m⁶AScore was correlated with them, but to a limited extent. Interestingly, HLAs and immune checkpoint expression, and immunophenoscore in patients with high-m⁶AScores were significantly lower than those in the low-m⁶AScore group. These suggested the m⁶AScores might be used to predict the feasibility of immunotherapy in patients. Results provided a distinctive perspective on m⁶A modification and theoretical basis for CC diagnosis, prognosis, clinical treatment strategies, and potential mechanism exploration.

Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique

Nowadays, so many people are living in world. If so many people are living, then the diseases are also increasing day by day due to adulterated and chemical content food. The people may suffer either from a small disease such as cold and cough or from a big disease such as cancer. In this work, we have discussed on the encephalon tumor or cancer which is a big problem nowadays. If we will consider about the whole world, then there are deficiency of clinical experts or doctors as compared to the encephalon tumor affected person. So, here, we have used an automatic classification of tumor by the help of particle swarm optimization (PSO)-based extreme learning machine (ELM) technique with the segmentation process by the help of improved fast and robust fuzzy C mean (IFRFCM) algorithm and most commonly feature reduction method used gray level co-occurrence matrix (GLCM) that may helpful to the clinical experts. Here, we have used the BraTs (“Multimodal Brain Tumor Segmentation Challenge 2020”) dataset for both the training and testing purpose. It has been monitored that our system has given better classification accuracy as an approximation of 99.47% which can be observed as a good outcome.

A Fog-Cluster Based Load-Balancing Technique

The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework.

CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.