Jul 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
The results suggest that merging explainability approaches with powerful machine learning can considerably boost early identification and risk assessment and contributes to enhanced healthcare decision-making, offering a scalable, interpretable and dependable solution for cardiovascular disease prediction.
Abstract
Diabetes is a major risk factor for the development of cardiovascular issues which contribute to cardiovascular disease (CVD) being a leading cause of mortality worldwide. However, traditional machine learning methods are not widely adopted in healthcare systems because they lack interpretability, which is important for early and accurate CVD risk prediction and for ruling out effective clinical intervention. In this research, a hybrid architecture is proposed that incorporates diabetes related datasets as well as explainable artificial intelligence (XAI) methodologies that could improve the prediction power and transparency of the models. The proposed approach combines different datasets at the level of features and includes rigorous data pre-processing to detect metabolic and cardiovascular risk factors. Some of the significant clinical parameters are age, BMI, glucose, cholesterol, and blood pressure. These are standardized to create a single dataset which may be utilized for predictive modelling. The employment of two XAI approaches, SHAP (SHapley Additive Explanations) with tree-based ensemble models and integrated gradients with transformer based topologies, ensures both performance and interpretability. The technique improves confidence and usefulness in clinical settings by offering accurate predictions and explanations for the model’s judgments that are relevant to the circumstance. It is also utilized for visual investigation of clinical correlations of diabetes and cardiovascular disease and identify crucial risk variables. The results suggest that merging explainability approaches with powerful machine learning can considerably boost early identification and risk assessment. The proposed approach contributes to enhanced healthcare decision-making, offering a scalable, interpretable and dependable solution for cardiovascular disease prediction.
Cardiovascular diseases (CVDs) remain a major cause of morbidity and mortality worldwide, emphasizing the need for reliable methods that can identify high-risk individuals at an early stage. Although machine learning and deep learning approaches have demonstrated considerable potential for cardiovascular risk prediction, their limited interpretability often restricts their acceptance in clinical decision-making. This study proposes an Explainable AI-Driven Decision Support System (XAI-DSS) for the early prediction of cardiovascular diseases by integrating intelligent clinical data preprocessing, feature selection, an ensemble learning-based prediction model, and explainable artificial intelligence. The framework processes heterogeneous cardiovascular risk factors, including demographic characteristics, blood pressure, cholesterol, glucose levels, electrocardiographic attributes, lifestyle factors, and other relevant clinical indicators. A hybrid feature-selection strategy is employed to identify the most informative risk variables, while an optimized ensemble classifier generates patient-specific CVD risk predictions. Explainability is incorporated using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to provide both global and patient-level interpretations of model decisions. Experimental evaluation demonstrated an accuracy of 96.42%, sensitivity of 95.81%, specificity of 96.87%, precision of 96.15%, F1-score of 95.98%, and area under the ROC curve (AUC) of 0.982. Compared with the selected baseline machine-learning model, the proposed XAI-DSS achieved an approximately 4.7% improvement in prediction accuracy and 5.3% improvement in F1-score. Explainability analysis further identified age, systolic blood pressure, cholesterol, maximum heart rate, fasting blood glucose, and chest-pain characteristics as influential factors contributing to cardiovascular risk predictions. The proposed framework therefore combines high predictive performance with transparent clinical reasoning, enabling healthcare professionals to understand the factors influencing individual risk assessments. The developed XAI-DSS can serve as a supportive screening framework for early cardiovascular risk stratification and informed clinical decision-making, subject to external clinical validation.
K. Sridhar, S. Swathi, S. Saranya et al.· International journal of com...· 0 citations
AIMS
To develop a cardiovascular disease (CVD) risk prediction model with improved accuracy and interpretability by integrating diverse risk factors and applying Automated Machine Learning (AutoML), thereby enhancing clinical utility over conventional models.
METHODS
This is a prospective cohort study. Data were obtained from the Multi-Ethnic Study of Atherosclerosis (MESA), including baseline and fifth follow-up visits, comprising 4713 participants. Exercise and dietary data were harmonized via Metabolic Equivalent of Task (MET) and Healthy Eating Index-2015 (HEI-2015), respectively. Predictor selection was performed using the Boruta algorithm alongside Random Forest (RF) error rate cross-validation. Logistic regression, four traditional machine learning algorithms, and H2O AutoML were each applied for model training and evaluation. Finally, the best-performing model was further interpreted using SHapley Additive exPlanations (SHAP).
RESULTS
A total of 21 predictors were selected, including age, sex, and Total Cholesterol (TC). Among the evaluated models, H2O AutoML outperformed other methods with an accuracy of 0.864, specificity of 0.892, precision of 0.610, F1 score of 0.670, and a Youden index of 0.635, achieving the highest AUC of 0.882 (0.846-0.918). SHAP analysis revealed the relative importance of predictors, with age, TC and Digit Symbol Score (DSS) ranking highest.
CONCLUSIONS
This study developed an AutoML-based CVD risk prediction model with superior discrimination and calibration, providing clinicians a practical tool for risk stratification. By enabling personalized prevention and early identification of high-risk individuals, this model has the potential to reduce CVD burden at the population level. Notably, DSS exhibited high importance and may represent a candidate risk marker.
Si-Min He, Ju-Ping Wang, Le Zhao et al.· International Journal of Car...· 0 citations
Diabetes is a chronic disease that significantly increases the risk of serious complications such as cardiovascular disorders and kidney failure. Early detection through predictive modeling can lead to timely interventions and significantly improve patient health outcomes. Several machine learning approaches have been proposed for predicting diabetes, but the main focus has been on improving prediction accuracy, while interpretability has received limited attention. To address this gap, we present a robust and explainable machine learning framework based on a stacked ensemble model that uses Random Forest, Support Vector Machine, and Gradient Boosting as base learners and Catboost as the meta-learner. The model was trained on the PIMA Indians Diabetes dataset using a preprocessing pipeline that included standard scaling, analysis of variance (ANOVA)- F-score-based feature selection, and class balancing with the synthetic minority oversampling technique (SMOTE). The proposed ensemble model outperformed the latest methods with an accuracy of 86%. We integrated explainable AI techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanation (SHAP) to enhance transparency, which provide both local and global interpretability by identifying the most influential features contributing to each prediction, thus supporting more informed and trustworthy decision-making in healthcare applications.
Cardiovascular disease (CVD) includes various conditions such as heart failure, stroke, and coronary artery disease, and is a leading cause of mortality globally. Timely interventions are important, and early prediction can enhance patient outcomes and prevent severe complications. This article presents CardioDiag, a Machine Learning approach designed to enhance the accuracy of CVD diagnostics in clinical decision-making. Our approach employs the Percentile Method to handle outliers and reduce skewness in categorical features. To handle class imbalance, it incorporates the Localized Random Affine Shadow Sampling (LoRAS) technique, which generates synthetic samples while preserving the original data distribution. Additionally, CardioDiag introduces an explainable artificial intelligence-based hybrid dimensionality reduction criterion (XAIH-DRC) for feature engineering. This method combines statistical correlation analysis with feature importance obtained from the Local Interpretable Model-agnostic Explanations (LIME) model, ensuring optimal feature selection. The effectiveness of CardioDiag is validated using two benchmark datasets: the Five States dataset and the Mendeley dataset, demonstrating its ability to generalize across diverse patient populations. Central to CardioDiag are two proposed ensemble classifiers: the Optimized Voting-based Ensemble Classifier (OVEC) and the Optimized Stacking-based Ensemble Classifier (OSEC). Both models are optimized through a hyperparameter search using 5-fold cross-validation. Experimental evidence shows that the voting-based OVEC in CardioDiag provides competitive and stable performance; however, the stacking-based OSEC consistently achieves superior predictive results. Specifically, the stacking-based OSEC demonstrates absolute gains of approximately 1% to 3% in accuracy and F1-score, and between 3% and 4% in precision and ROC-AUC compared to existing individual and ensemble methods. Further analysis using calibration and decision curve evaluations confirms reliable probability estimation and clinically meaningful utility across a range of decision thresholds. These findings underscore the effectiveness of the CardioDiag approach in enhancing diagnostic accuracy and reliability. The observed improvements over state-of-the-art models support its utility in clinical decision-making and aid in the early diagnosis of CVD.
M. J. Khan, B. Raza, Adnan Nadeem· PeerJ Computer Science· 0 citations
Early detection of Type 2 Diabetes Mellitus (T2DM remains a critical challenge in preventive healthcare due to the complex interplay of clinical, demographic, and lifestyle factors. This study proposes an Integrated Predictive, Explainable, and Causal Machine Learning Framework for early detection of Type 2 diabetes, incorporating uncertainty quantification and fairness analysis to enhance clinical reliability and generalizability. The proposed framework is evaluated using the Pima Indians Diabetes Dataset, comprising clinical measurements such as glucose concentration, body mass index, blood pressure, and age. An ensemble predictive architecture centered on XGBoost achieves superior classification performance, attaining an accuracy of 87.4% and a ROC-AUC of 0.91 under stratified 10-fold cross-validation. Model transparency is ensured through SHAP-based explainability, enabling clinicians to identify dominant risk contributors, particularly plasma glucose levels and BMI. To move beyond correlation-based inference, a Structural Causal Model is employed to estimate the causal influence of modifiable risk factors on diabetes onset. Additionally, fairness-aware evaluation is conducted using demographic parity and equal opportunity metrics, revealing reduced prediction bias across age and gender subgroups after fairness constraints are applied.
C. S. Reddy, Mohan Annamalai· International Conference on...· 0 citations
This systematic review examines contemporary machine learning methods and explainable AI procedures engaged prediction of heart diseases and classification and applies explainability AI heart disease prediction models that will ease the process and also make the upcoming system with better progress and more trustworthy, operative and unfailing clinical solutions that makes system decision support one.
R. Jain, Sachin H. Patel· International journal of com...· 0 citations
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