A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease

Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the US

BACKGROUND

COVID-19 has been linked to acute kidney injury (AKI) and chronic kidney disease (CKD), but machine learning (ML) models predicting these risks post-pandemic have been absent. We aimed to use large electronic health records (EHR) and ML algorithms to predict the incidence of AKI and CKD during the post-pandemic period, assess the necessity of including COVID-19 infection history as a predictor, and develop a practical webpage application for clinical use.

METHODS

National EHR data from TriNetX, emulating a prospective cohort of 104,565 patients from 07/01/2022 to 03/31/2024, were used. A total of 69 baseline variables were included, with demographics, comorbidities, lab test results, vital signs, medication histories, hospitalization visits, and COVID-19-related variables. Prediction windows of 1 month and 1 year were defined to assess AKI and CKD incidence. Eight machine learning models, primarily including extreme gradient boosting (XGBoost), neural network, and random forest (RF), were applied. Cross-validation and model tuning were conducted during the training process. Model performance was evaluated using six metrics, including the area under the receiver-operating-characteristic curve (AUROC). A combination of model-driven, data-driven, and clinical-driven methods was employed to identify the final models. An application with the final models was built using the R Shiny framework.

FINDINGS

The final models, incorporating 9 variables-primarily including eGFR, inpatient visit number, and number of COVID-19 infections-were selected. XGBoost demonstrated the best performance for predicting the incidence of AKI in 1 month (AUROC = 0.803), AKI in 1 year (AUROC = 0.799), and CKD in 1 year (AUROC = 0.894). Random Forest (RF) was selected for predicting the incidence of CKD in 1 month (AUROC = 0.896). A comparison of AUROC with and without COVID-19 infection confirmed its importance as a critical predictor in the model. The final models were translated into a convenient tool to facilitate their use in clinical settings.

INTERPRETATION

Our study demonstrates the applicability of using large national EHR data in developing high-performance machine learning models to predict AKI and CKD risks in the post-COVID-19 period. Incorporating the number of COVID-19 infections in the past year showed improved prediction performance and should be considered in future models for kidney disease prediction. A user-friendly application was created to support clinicians in risk assessment and surveillance.

FUNDING

Artificial Intelligence and Biomedical Informatics Pilot Funding, Penn State College of Medicine.

A population based optimization of convolutional neural networks for chronic kidney disease prediction

Chronic kidney disease (CKD) is a global public health concern, and the timely detection of the disease is priceless. Most of the classical machine learning models have the major drawbacks of being unsophisticated, non-robust, and non-accurate. This research work is therefore seeking to introduce OptiNet-CKD, a paradigm based on a DNN that has been integrated with a developed population optimization algorithm (POA) for CKD prediction optimization. POA is unlike gradient-based optimization methods in that it uses an initialized population of networks and perturbs their weight values to provide a broader exploration of the solution space. The model is more robust and less likely to overfit, and the predictions are likely to be more accurate since this approach helps to avoid the local minima problem suffered by gradient-based optimizers. To preprocess it for DNN learning, a CKD dataset with 400 records containing numerical and categorical features was imputed for missing data and scaled for its features. The model was evaluated using performance metrics such as accuracy, precision, recall, F1-score, and ROC AUC. OptiNet-CKD achieved 100% accuracy, 1.0 precision, 1.0 recall, 1.0 F1-score, and 1.0 ROC-AUC from traditional models (logistic regression, decision trees) and even fundamental deep neural networks. Results show that OptiNet-CKD is a reliable and robust prediction method for CKD, with more substantial generalization and performance than the existing methods. A combination of DNN and POA constitutes a promising approach for medical data analysis, especially for the diagnosis of CKD. POA expands the solution space, helping to expunge the model from falling into local minima and giving the model increased power in generalizing complicated medical data. Based on the simplicity of the algorithm, together with the structured formula and the extractions made in the preprocessing step, this framework can be extended to other medical conditions with similar data complexities, providing a potent tool for improving diagnostic accuracy in healthcare.

Kidney cancer diagnosis and surgery selection by double decker convolutional neural network from CT scans combined with great wall construction algorithm

One of the most prevalent cancers in the world is kidney cancer (KC). A precise diagnosis, which is influenced by a number of variables, such as the size or volume of the tumor, the types and stages of the cancer, etc., is essential for the treatment of patients with kidney cancer. In this work two main types of kidney cancer: normal and abnormal, using the accessible KiTS21 dataset of contrast-enhanced CT scans and associated data from patients. Many of these techniques show poor accuracy, which raises doubts regarding their efficiency and dependability. To overcome these limitations, this paper presents the use of a double-decker convolutional neural network with the great wall construction algorithm (DDCNN-GWCA). Hybrid quick conventional bilateral filter improves the quality of pre-processed data by reducing noise while preserving crucial information by using the KiTS21 dataset. Practical Quantum K-Means Clustering is used for segmentation to improve detection efficiency and accuracy. The Q-value Regularized Transformer (QT) is a feature extraction method that combines the power of transformers with Q-value regularization to capture the relevant features. A Double-Decker Convolutional Neural Network's multi-layered architecture is used for classification to identify the classes. The Great Wall Construction Algorithm is an innovative optimization technique that optimizes the hyperparameters of the Double Decker Convolutional Neural Network (DDCNN), ensuring enhanced performance. It obtained scores of 98.9% for the KiTS21 dataset. These results demonstrate the strategy's ability to outperform existing methods and open the way for major advances in the diagnosis of kidney cancer.

Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions

Chronic Kidney Disease (CKD) represents a significant global health challenge, contributing to increased morbidity and mortality rates. This review paper explores the current landscape of machine learning (ML) techniques employed in CKD prediction and diagnosis, highlighting recent trends, inherent challenges, innovative solutions, and future directions. Through an extensive literature survey, we identified key limitations and challenges, including the use of small datasets, the absence of stage-specific predictions, insufficient focus on model interpretability, and a lack of discussions on safeguarding patient privacy in managing sensitive CKD data. We considered these limitations and challenges as research gaps, and this review paper aims to address them. We emphasize the potential of Generative AI to augment dataset sizes, thereby enhancing model performance and reliability. To address the lack of stage-specific predictions, we highlight the need for effective multi-class models to accurately predict CKD stages, enabling tailored treatments and improved patient outcomes. Furthermore, we discuss the critical importance of model interpretability, utilizing methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to ensure transparency and trust among healthcare professionals. Privacy concerns surrounding sensitive patient data are also addressed. We present innovative privacy-preserving solutions using technologies, such as homomorphic encryption, federated learning, and blockchain. These solutions facilitate collaboration across institutions while maintaining patient confidentiality and addressing challenges related to limited generalizability and reproducibility in CKD prediction. This review informs healthcare professionals and researchers about advancements in ML for CKD prediction, to improve patient outcomes and address research gaps.

Kidney Disease Segmentation and Classification Using Firefly Sigma Seeker and MagWeight Rank Techniques

Deep learning models possess the ability to precisely analyze medical images such as MRI, CT scans, and ultrasound images. This automated diagnostic process facilitates the early detection of kidney disease by identifying any abnormalities or signs of disease. Consequently, it allows for timely intervention and treatment, while also reducing the need for manual interpretation by radiologists or clinicians. As a result, the diagnosis process is expedited, leading to improved efficiency in healthcare. The proposed technique focuses on enhancing parallel convolutional layer architectures in kidney disease segmentation through the utilization of advanced optimization techniques. This approach integrates Firefly Sigma Seeker and MagWeight Rank methodologies into the design of these architectures. The Firefly Sigma Seeker methodology dynamically adjusts key parameters related to standard deviation during training to enable early stopping in the initial phase. Subsequently, MagWeight Rank optimizes parameter weighting and ranking within the architecture to prune less important weights, thereby reducing computational time and overfitting. By leveraging these techniques, the parallel convolutional layers are specifically tailored for kidney disease segmentation tasks. Finally, the Multi-Stream Neural Network (MSNN) efficiently classifies kidney disease. Through extensive experimentation and evaluation on kidney disease segmentation datasets, a comparative analysis of different architectures was conducted in terms of segmentation accuracy, computational efficiency, and scalability. The proposed framework achieves optimal segmentation performance, with an accuracy of 98.2%, a minimized loss of 0.1, a reduced computational time of 15 min and 4 s, and successfully avoids overfitting.

Early prediction of CKD from time series data using adaptive PSO optimized echo state networks

Chronic Kidney Disease (CKD) is a significant problem in today's healthcare since it is challenging to detect until it has improved significantly, which increases medical expenses. If CKD was detected early, the patient might qualify for more effective treatment and prevent the disease from spreading further. Presently, existing methods that effectively detect CKD cannot detect symptoms early on. This problem motivates researchers to work on a predictive model that successfully detects disease symptoms in the early stages. This study introduces a novel Adaptive Particle Swarm Optimization (APSO)-optimized Echo State Network (ESN) model designed to overcome key limitations of existing methods. ESNs, while effective in processing temporal sequences, are highly sensitive to hyperparameter settings such as spectral radius, input scaling, and sparsity, which directly impact stability, memory retention, and predictive Classification Accuracy (CA). To address this, APSO optimizes these hyperparameters dynamically, ensuring a balanced trade-off between stability and computational efficiency. Moreover, Random Matrix Theory (RMT) is integrated into APSO to regulate the spectral radius, enhancing the ESN's capability to handle long-term dependencies while maintaining stability in training. This investigation exploited the Medical Information Mart for Intensive Care-III (MIMIC-III) dataset to train the model they developed. The proposed method employs this data collection to analyze the highly complex temporal sequences signifying CKD is present. The hyperparameters of the ESN, such as the range of the spectral region and the input data sizing, can be optimized in real-time with APSO by applying Random Matrix Theory (RMT). Compared with different recognized models, such as conventional ESN and standard M, the recommended APSO + ESN proved to have higher CA in medical investigations. The APSO + ESN improved the subsequent highest-performing model by 2% in recall and 3% in precision and attained a CA of 99.6%.

Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review

The escalating demand for chronic disease management has presented substantial challenges to traditional methods. However, the emergence of Internet of Things (IoT) and artificial intelligence (AI) technologies offers a potential resolution by facilitating more precise chronic disease management through data-driven strategies. This review concentrates on the utilization of IoT mobile sensing devices in managing major chronic diseases such as cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes. It scrutinizes their efficacy in disease diagnosis and management when integrated with machine learning algorithms, such as ANN, SVM, RF, and deep learning models. Through an exhaustive literature review, this study dissects how these technologies aid in risk assessment, personalized treatment planning, and disease management. This research addresses a gap in the existing literature concerning the application of IoT and AI technologies in the management of specific chronic diseases. It particularly demonstrates methodological novelty by introducing advanced models based on deep learning, tight frame-based methodologies and real-time monitoring systems. This review employs a rigorous examination method, which includes systematically searching relevant databases, filtering literature that meets specific inclusion and exclusion criteria, and adopting quality assessment tools to ensure the rigor of selected studies. This study identifies potential biases and weaknesses related to data collection, algorithm selection, and user interaction. The research demonstrates that platforms integrating IoT and machine learning algorithms for chronic disease monitoring and management are not only technically viable but also yield substantial economic and social advantages in real-world applications. Future studies could investigate the use of quantum computing for processing vast medical datasets and novel techniques that merge biosensors with nanotechnology for drug delivery and disease surveillance. Furthermore, this paper examines recent progress in medical image reconstruction, emphasizing tight frame-based methodologies. We discuss the principles, benefits, and constraints of these methods, assessing their efficacy across diverse application contexts.

Enhancing individual glomerular filtration rate assessment: can we trust the equation? Development and validation of machine learning models to assess the trustworthiness of estimated GFR compared to measured GFR

BACKGROUND

Creatinine-based estimated glomerular filtration rate (eGFR) equations are widely used in clinical practice but exhibit inherent limitations. On the other side, measuring GFR is time consuming and not available in routine clinical practice. We developed and validated machine learning models to assess the trustworthiness (i.e. the ability of equations to estimate measured GFR (mGFR) within 10%, 20% or 30%) of the European Kidney Function Consortium (EKFC) equation at the individual level.

METHODS

This observational study used data from European and US cohorts, comprising 22,343 participants of all ages with available mGFR results. Four machine learning and two traditional logistic regression models were trained on a cohort of 9,202 participants to predict the likelihood of the EKFC creatinine-derived eGFR falling within 30% (p30), 20% (p20) or 10% (p10) of the mGFR value. The algorithms were internally and then externally validated on cohorts of respectively 3,034 and 10,107 participants. The predictors included in the models were creatinine, age, sex, height, weight, and EKFC.

RESULTS

The random forest model was the most robust model. In the external validation cohort, the model achieved an area under the curve of 0.675 (95%CI 0.660;0.690) and an accuracy of 0.716 (95%CI 0.707;0.725) for the P30 criterion. Sensitivity was 0.756 (95%CI 0.747;0.765) and specificity was 0.485 (95%CI 0.460; 0.511) at the 80% probability level that EKFC falls within 30% of mGFR. At the population level, the PPV of this machine learning model was 89.5%, higher than the EKFC P30 of 85.2%. A free web-application was developed to allow the physician to assess the trustworthiness of EKFC at the individual level.

CONCLUSIONS

A strategy using machine learning model marginally improves the trustworthiness of GFR estimation at the population level. An additional value of this approach lies in its ability to provide assessments at the individual level.

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.

Deep-kidney: an effective deep learning framework for chronic kidney disease prediction

Chronic kidney disease (CKD) is one of today's most serious illnesses. Because this disease usually does not manifest itself until the kidney is severely damaged, early detection saves many people's lives. Therefore, the contribution of the current paper is proposing three predictive models to predict CKD possible occurrence within 6 or 12 months before disease existence namely; convolutional neural network (CNN), long short-term memory (LSTM) model, and deep ensemble model. The deep ensemble model fuses three base deep learning classifiers (CNN, LSTM, and LSTM-BLSTM) using majority voting technique. To evaluate the performance of the proposed models, several experiments were conducted on two different public datasets. Among the predictive models and the reached results, the deep ensemble model is superior to all the other models, with an accuracy of 0.993 and 0.992 for the 6-month data and 12-month data predictions, respectively.

Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis

BACKGROUND

Chronic kidney disease (CKD) is a common condition that can lead to serious health complications. Artificial Intelligence (AI) has shown the potential to improve the prediction of CKD progression, offering increased accuracy over traditional methods. Therefore, this systematic review and meta-analysis examine the diagnostic performance of various AI models in predicting CKD.

METHOD

Search was performed in different databases for studies reporting the diagnostic accuracy of AI-based prediction models for the progression of CKD. Meanwhile, pre-defined eligibility criteria were used for the selection of studies. Pooled sensitivity, specificity, and area under curve (AUC) were calculated utilizing Meta-disc 1.4. Quality assessment was performed using the prediction model risk of bias assessment tool (PROBAST).

RESULTS

A total of 33 studies were included. The pooled sensitivity of prediction tools was 0.43 (95% CI, 0.41-0.44, I2 = 99.3%, p < 0.01). A significant difference (p < 0.01) was also observed in the pooled specificity 0.92 (95% CI, 0.91-0.92, I2 = 99.5%). Positive likelihood ratio (PLP) and negative likelihood ratio (NLR) were 5.12 (95% CI: 3.60-7.27, I2 = 91.3%, p < 0.01) and 0.28 (95% CI: 0.21-0.37, I2 = 99.3%, p < 0.01), respectively and AUC was 0.89, suggesting a diagnostic accuracy of AI-based prediction models for the progression of CKD.

CONCLUSIONS

This study demonstrates the promising potential of AI models in predicting CKD progression. However, further efforts are needed to optimize model performance, particularly in balancing sensitivity and specificity to ensure generalizability across diverse populations. Limitations of this study include the potential for overfitting in certain AI models due to imbalanced datasets. The high heterogeneity and the lack of standardized predictors limit the generalizability of findings across different populations.

Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate

In clinical practice, the glomerular filtration rate (GFR), a measurement of kidney functioning, is normally calculated using equations, such as the European Kidney Function Consortium (EKFC) equation. Despite being the most general equation, EKFC, just like previously proposed approaches, can still struggle to achieve satisfactory performance, limiting its clinical applicability. As a possible solution, recently machine learning (ML) has been investigated to improve GFR prediction, nonetheless the literature still lacks a general and multi-center study. Using a dataset with 19,629 patients from 13 cohorts, we investigate if ML can improve GFR prediction in comparison to EKFC. More specifically, we compare diverse ML methods, which were allowed to use age, sex, serum creatinine, cystatin C, height, weight and BMI as features, in internal and external cohorts against EKFC. The results show that the most performing ML method, random forest (RF), and EKFC are very competitive where RF and EKFC achieved respectively P10 and P30 values of 0.45 (95% CI 0.44;0.46) and 0.89 (95% CI 0.88;0.90), whereas EKFC yielded 0.44 (95% CI 0.43; 0.44) and 0.89 (95% CI 0.88; 0.90), considering the entire cohort. Small differences were, however, observed in patients younger than 12 years where RF slightly outperformed EKFC.

Integrating neural networks with advanced optimization techniques for accurate kidney disease diagnosis

Kidney diseases pose a significant global health challenge, requiring precise diagnostic tools to improve patient outcomes. This study addresses this need by investigating three main categories of renal diseases: kidney stones, cysts, and tumors. Utilizing a comprehensive dataset of 12,446 CT whole abdomen and urogram images, this study developed an advanced AI-driven diagnostic system specifically tailored for kidney disease classification. The innovative approach of this study combines the strengths of traditional convolutional neural network architecture (AlexNet) with modern advancements in ConvNeXt architectures. By integrating AlexNet's robust feature extraction capabilities with ConvNeXt's advanced attention mechanisms, the paper achieved an exceptional classification accuracy of 99.85%. A key advancement in this study's methodology lies in the strategic amalgamation of features from both networks. This paper concatenated hierarchical spatial information and incorporated self-attention mechanisms to enhance classification performance. Furthermore, the study introduced a custom optimization technique inspired by the Adam optimizer, which dynamically adjusts the step size based on gradient norms. This tailored optimizer facilitated faster convergence and more effective weight updates, imporving model performance. The model of this study demonstrated outstanding performance across various metrics, with an average precision of 99.89%, recall of 99.95%, and specificity of 99.83%. These results highlight the efficacy of the hybrid architecture and optimization strategy in accurately diagnosing kidney diseases. Additionally, the methodology of this paper emphasizes interpretability and explainability, which are crucial for the clinical deployment of deep learning models.

Three-dimensional convolutional neural network-based classification of chronic kidney disease severity using kidney MRI

A three-dimensional convolutional neural network model was developed to classify the severity of chronic kidney disease (CKD) using magnetic resonance imaging (MRI) Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) imaging. Seventy-three patients with severe renal dysfunction (estimated glomerular filtration rate [eGFR] < 30 mL/min/1.73 m2, CKD stage G4-5); 172 with moderate renal dysfunction (30 ≤ eGFR < 60 mL/min/1.73 m2, CKD stage G3a/b); and 76 with mild renal dysfunction (eGFR ≥ 60 mL/min/1.73 m2, CKD stage G1-2) participated in this study. The model was applied to the right, left, and both kidneys, as well as to each imaging method (T1-weighted IP/OP/WO images). The best performance was obtained when using bilateral kidneys and IP images, with an accuracy of 0.862 ± 0.036. The overall accuracy was better for the bilateral kidney models than for the unilateral kidney models. Our deep learning approach using kidney MRI can be applied to classify patients with CKD based on the severity of kidney disease.

Machine learning techniques to predict the risk of developing diabetic nephropathy: a literature review

Purpose

Diabetes is a major public health challenge with widespread prevalence, often leading to complications such as Diabetic Nephropathy (DN)-a chronic condition that progressively impairs kidney function. In this context, it is important to evaluate if Machine learning models can exploit the inherent temporal factor in clinical data to predict the risk of developing DN faster and more accurately than current clinical models.

Methods

Three different databases were used for this literature review: Scopus, Web of Science, and PubMed. Only articles written in English and published between January 2015 and December 2022 were included.

Results

We included 11 studies, from which we discuss a number of algorithms capable of extracting knowledge from clinical data, incorporating dynamic aspects in patient assessment, and exploring their evolution over time. We also present a comparison of the different approaches, their performance, advantages, disadvantages, interpretation, and the value that the time factor can bring to a more successful prediction of diabetic nephropathy.

Conclusion

Our analysis showed that some studies ignored the temporal factor, while others partially exploited it. Greater use of the temporal aspect inherent in Electronic Health Records (EHR) data, together with the integration of omics data, could lead to the development of more reliable and powerful predictive models.

Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease

In today's modern era, chronic kidney disease stands as a significantly grave ailment that detrimentally impacts human life. This issue is progressively escalating in both developed and developing nations. Precise and timely identification of chronic kidney disease is imperative for the prevention and management of kidney failure. Historical methods of diagnosing chronic kidney disease have often been deemed unreliable on several fronts. To distinguish between healthy individuals and those afflicted by chronic kidney disease, dependable and effective non-invasive techniques such as machine learning models have been adopted. In our ongoing research, we employ various machine learning models, encompassing logistic regression, random forest, decision tree, k-nearest neighbor, and support vector machine utilizing four kernel functions (linear, Laplacian, Bessel, and radial basis kernels), to forecast chronic kidney disease. The dataset used constitutes records from a case-control study involving chronic kidney disease patients in district Buner, Khyber Pakhtunkhwa, Pakistan. For comparative evaluation of the models in terms of classification and accuracy, diverse performance metrics, including accuracy, Brier score, sensitivity, Youden's index, and F1 score, were computed.

Snake-Efficient Feature Selection-Based Framework for Precise Early Detection of Chronic Kidney Disease

Chronic kidney disease (CKD) refers to impairment of the kidneys that may worsen over time. Early detection of CKD is crucial for saving millions of lives. As a result, several studies are currently focused on developing computer-aided systems to detect CKD in its early stages. Manual screening is time-consuming and subject to personal judgment. Therefore, methods based on machine learning (ML) and automatic feature selection are used to support graders. The goal of feature selection is to identify the most relevant and informative subset of features in a given dataset. This approach helps mitigate the curse of dimensionality, reduce dimensionality, and enhance model performance. The use of natural-inspired optimization algorithms has been widely adopted to develop appropriate representations of complex problems by conducting a blackbox optimization process without explicitly formulating mathematical formulations. Recently, snake optimization algorithms have been developed to identify optimal or near-optimal solutions to difficult problems by mimicking the behavior of snakes during hunting. The objective of this paper is to develop a novel snake-optimized framework named CKD-SO for CKD data analysis. To select and classify the most suitable medical data, five machine learning algorithms are deployed, along with the snake optimization (SO) algorithm, to create an extremely accurate prediction of kidney and liver disease. The end result is a model that can detect CKD with 99.7% accuracy. These results contribute to our understanding of the medical data preparation pipeline. Furthermore, implementing this method will enable health systems to achieve effective CKD prevention by providing early interventions that reduce the high burden of CKD-related diseases and mortality.

Improved Survival Analyses Based on Characterized Time-Dependent Covariates to Predict Individual Chronic Kidney Disease Progression

Kidney diseases can cause severe morbidity, mortality, and health burden. Determining the risk factors associated with kidney damage and deterioration has become a priority for the prevention and treatment of kidney disease. This study followed 497 patients with stage 3-5 chronic kidney disease (CKD) who were treated at the ward of Taipei Veterans General Hospital from January 2006 to 2019 in Taiwan. The patients underwent 3-year-long follow-up sessions for clinical measurements, which occurred every 3 months. Three time-dependent survival models, namely the Cox proportional hazard model (Cox PHM), random survival forest (RSF), and an artificial neural network (ANN), were used to process patient demographics and laboratory data for predicting progression to renal failure, and important features for optimal prediction were evaluated. The individual prediction of CKD progression was validated using the Kaplan-Meier estimation method, based on patients' true outcomes during and beyond the study period. The results showed that the average concordance indexes for the cross-validation of the Cox PHM, ANN, and RSF models were 0.71, 0.72, and 0.89, respectively. RSF had the best predictive performances for CKD patients within the 3 years of follow-up sessions, with a sensitivity of 0.79 and specificity of 0.88. Creatinine, age, estimated glomerular filtration rate, and urine protein to creatinine ratio were useful factors for predicting the progression of CKD patients in the RSF model. These results may be helpful for instantaneous risk prediction at each follow-up session for CKD patients.

A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images

Lung cancer is a fatal disease caused by an abnormal proliferation of cells in the lungs. Similarly, chronic kidney disorders affect people worldwide and can lead to renal failure and impaired kidney function. Cyst development, kidney stones, and tumors are frequent diseases impairing kidney function. Since these conditions are generally asymptomatic, early, and accurate identification of lung cancer and renal conditions is necessary to prevent serious complications. Artificial Intelligence plays a vital role in the early detection of lethal diseases. In this paper, we proposed a modified Xception deep neural network-based computer-aided diagnosis model, consisting of transfer learning based image net weights of Xception model and a fine-tuned network for automatic lung and kidney computed tomography multi-class image classification. The proposed model obtained 99.39% accuracy, 99.33% precision, 98% recall, and 98.67% F1-score for lung cancer multi-class classification. Whereas, it attained 100% accuracy, F1 score, recall and precision for kidney disease multi-class classification. Also, the proposed modified Xception model outperformed the original Xception model and the existing methods. Hence, it can serve as a support tool to the radiologists and nephrologists for early detection of lung cancer and chronic kidney disease, respectively.

A Unified Framework for Monitoring Social Distancing and Face Mask Wearing Using Deep Learning: An Approach to Reduce COVID-19 Risk

Corona Virus Disease 2019 (COVID-19) is caused by Severe Acute Syndrome Corona Virus 2 (SARS-COV-2). It has become a pandemic disease of the 21st century, killing many lives. During this pandemic situation, precautious measures like social distancing and wearing face mask are being followed globally to break the COVID chain. A pre-programmed viewing system is needed to monitor whether these COVID-19 appropriate behaviours are being followed by the commoners and to ensure COVID-19 preventive measures are followed appropriately. In this work, a deep learning based predictive model and live risk analysis application has been proposed, which detects the high-risk prone areas based on social distancing measures among individuals and face mask wearing tendency of the commoners. The proposed system utilizes ImageNet-1000 dataset for human detection using You Only Look Once (YOLOv3) object detection algorithm; Residual Neural Network (ResNet50v2) uses Kaggle dataset and Real-World Masked Face Dataset (RMFD) for detecting if the persons are face masked or not. Detected human beings (in side-view) are transformed to top view using Top-View Transform Model (TVTM) followed by the calculation of interpersonal distance between the pedestrians and categorized them into three classes include high risk, medium risk, low risk. This unified predictive model provided an accuracy of 97.66%, precision of 97.84%, and F1-Score of 97.92%.

COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net

Segmentation of pneumonia lesions from Lung CT images has become vital for diagnosing the disease and evaluating the severity of the patients during the COVID-19 pandemic. Several AI-based systems have been proposed for this task. However, some low-contrast abnormal zones in CT images make the task challenging. The researchers investigated image preprocessing techniques to accomplish this problem and to enable more accurate segmentation by the AI-based systems. This study proposes a COVID-19 Lung-CT segmentation system based on histogram-based non-parametric region localization and enhancement (LE) methods prior to the U-Net architecture. The COVID-19-infected lung CT images were initially processed by the LE method, and the infected regions were detected and enhanced to provide more discriminative features to the deep learning segmentation methods. The U-Net is trained using the enhanced images to segment the regions affected by COVID-19. The proposed system achieved 97.75%, 0.85, and 0.74 accuracy, dice score, and Jaccard index, respectively. The comparison results suggested that the use of LE methods as a preprocessing step in CT Lung images significantly improved the feature extraction and segmentation abilities of the U-Net model by a 0.21 dice score. The results might lead to implementing the LE method in segmenting varied medical images.

An Explainable AI driven Decision Support System for COVID-19 Diagnosis using Fused Classification and Segmentation

The coronavirus has caused havoc on billions of people worldwide. The Reverse Transcription Polymerase Chain Reaction(RT-PCR) test is widely accepted as a standard diagnostic tool for detecting infection, however, the severity of infection can't be measured accurately with RT-PCR results. Chest CT Scans of infected patients can manifest the presence of lesions with high sensitivity. During the pandemic, there is a dearth of competent doctors to examine chest CT images. Therefore, a Guided Gradcam based Explainable Classification and Segmentation system (GGECS) which is a real-time explainable classification and lesion identification decision support system is proposed in this work. The classification model used in the proposed GGECS system is inspired by Res2Net. Explainable AI techniques like GradCam and Guided GradCam are used to demystify Convolutional Neural Networks (CNNs). These explainable systems can assist in localizing the regions in the CT scan that contribute significantly to the system's prediction. The segmentation model can further reliably localize infected regions. The segmentation model is a fusion between the VGG-16 and the classification network. The proposed classification model in GGECS obtains an overall accuracy of 98.51 % and the segmentation model achieves an IoU score of 0.595.

AMSFMap Methodology to improve prediction accuracy of CNN model for Covid19 using X-ray images

A serious medical issue reported at the center of media worldwide, Since December, 2019 is the Covid19 pandemic. As declared by World Health Organization, confirmed cases of Covid19 have been 579,893,790 including 6,415,070 deaths as of 29 July 2022. Even new cases reported in last 24 hours are 20,409 in India. This needs to diagnose and timely treatment of Covid-19 is essential to prevent hurdles including death. The author developed deep learning based Covid19 diagnosis and severity prediction models using x-ray images with hope that this technology can increase access to radiology expertise in remote places where availability of expert radiologist is limited. The researchers proposed and implemented Attentive Multi Scale Feature map based deep Network (AMSF-Net) for x- ray image classification with improved accuracy. In binary classification, x-ray images are classified as normal or Covid19. Multiclass classification classifies x-ray images into mild, moderate or severe infection of Covid19. The researchers utilized lower layers features in addition to features from highest level with different scale to increase ability of CNN to learn fine-grained features. Channel attention also incorporated to amplify features of important channels. ROI based cropping and AHE employed to enhance content of training image. Image augmentation utilized to increase dataset size. To address the issue of the class imbalance problem, focal loss has been applied. Sensitivity, precision, accuracy and F1 score metrics are used for performance evaluation. The author achieved 78% accuracy for binary classification. Precision, recall and F1 score values for positive class is 85, 67 and 75, respectively while 73, 88 and 80 for negative class. Classification accuracy of mild, moderate and sever class is 90, 97 and 96. Average accuracy of 95 % achieved with superior performance compared to existing methods.

A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions

Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain's requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario's requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has been identified that a sub-set of ML algorithms are more efficient for timely predictions under specific healthcare scenarios, and that is why the envisioned ML catalogue prioritizes the ML algorithms to be used, depending on the scenarios' nature and needed metrics. Further evaluation must be performed considering additional scenarios, involving state-of-the-art techniques (e.g., cloud deployment, federated ML) for improving the mechanism's efficiency.

Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis

The aim of this study was to assess the possibility of using deep convolutional neural networks (DCNNs) to develop an effective method for diagnosing osteoporosis based on CT images of the spine. The research material included the CT images of L1 spongy tissue belonging to 100 patients (50 healthy and 50 diagnosed with osteoporosis). Six pre-trained DCNN architectures with different topological depths (VGG16, VGG19, MobileNetV2, Xception, ResNet50, and InceptionResNetV2) were used in the study. The best results were obtained for the VGG16 model characterised by the lowest topological depth (ACC = 95%, TPR = 96%, and TNR = 94%). A specific challenge during the study was the relatively small (for deep learning) number of observations (400 images). This problem was solved using DCNN models pre-trained on a large dataset and a data augmentation technique. The obtained results allow us to conclude that the transfer learning technique yields satisfactory results during the construction of deep models for the diagnosis of osteoporosis based on small datasets of CT images of the spine.