Jul 2026· Journal of Electronic & Information Systems· Vol 8, pp. 14-42· 0 citations· 33 references
TL;DR
This study aims to design and evaluate a Model Context Protocol–enabled Custom GPT framework that integrates ML-based predictive models with external healthcare systems to deliver context-aware, explainable, and clinically actionable heart disease risk predictions and paves the way for broader adoption of agentic AI in clinical workflows.
Abstract
Cardiovascular diseases (CVDs) are among the leading causes of mortality worldwide. Early detection and risk stratification are critical for preventive care. Traditional machine learning (ML) models can predict heart disease risk but often lack interpretability and fail to integrate with real-time clinical data. Recent advances in fine-tuned large language models (Custom GPT) offer natural language explanations but are limited by insufficient interoperability with heterogeneous healthcare data sources. This study aims to design and evaluate a Model Context Protocol (MCP)–enabled Custom GPT framework that integrates ML-based predictive models with external healthcare systems—including EHRs, laboratory APIs, and wearable devices—to deliver context-aware, explainable, and clinically actionable heart disease risk predictions. The experimental evaluation was conducted on a validated cardiovascular dataset containing 303 patient records and 14 clinically relevant attributes derived from publicly available clinical repositories. Experimental evaluation demonstrated improved predictive accuracy (approximately 88% with the XGBoost ensemble) and robustness compared to standalone models. MCP integration enabled dynamic contextual awareness, reduced latency in tool orchestration, and enriched interpretability through RAG-based explanations. Clinician and patient evaluations confirmed enhanced usability and transparency. This approach paves the way for broader adoption of agentic AI in clinical workflows.
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.
K. Deepthi, P. Bhargavi· International journal of com...· 0 citations
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
Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide, necessitating improved approaches for early detection and risk stratification. Traditional cardiovascular risk assessment models often demonstrate limited predictive accuracy because they rely on a restricted number of clinical variables and linear statistical relationships. Artificial intelligence (AI)-driven predictive analytics has emerged as a promising solution capable of processing large, complex, and multidimensional healthcare datasets to improve cardiovascular risk prediction and clinical decision-making. This systematic review aimed to evaluate the effectiveness of AI-based predictive models for the early detection and risk stratification of CVD. A comprehensive literature search was conducted across PubMed/MEDLINE, Scopus, Web of Science, Embase, CINAHL, IEEE Xplore, and the Cochrane Library for studies published between January 2015 and December 2025. A total of 130 studies involving approximately 12.8 million participants met the eligibility criteria and were included in the review. The findings demonstrated that machine learning (ML) and deep learning (DL) models consistently outperformed conventional cardiovascular risk prediction tools, achieving area under the receiver operating characteristic curve (AUC) values ranging from 0.80-0.99 across various cardiovascular conditions. The strongest evidence was observed in the prediction of coronary artery disease, heart failure, atrial fibrillation, stroke, and major adverse cardiovascular events. Electronic health records, electrocardiography, medical imaging, wearable devices, and multimodal datasets were the most commonly utilized data sources. Despite their promising performance, challenges related to external validation, interpretability, algorithmic bias, and clinical implementation remain.
N. Muruganandan, P. Prathiba, Khyati Rajeshkumar Patel et al.· International Journal of Res...· 0 citations
The proposed approach uses a Quantum Neural Network for machine learning for machine learning in an intelligent Cardiovascular Disease (CVD) prediction system that has the highest sensitivity and specificity in the current literature, matching exact expert opinions.
Hutashani B. Rayate, Mangesh D. Nikose, Prakash G. Burade· International journal of com...· 0 citations
Cardiovascular disease is still one of the leading causes of death, and hence, the early prediction of risk is a very important task in preventive medicine. Although recent studies have shown encouraging results in the application of machine learning algorithms to the prediction of heart disease, it has been noticed that most of the algorithms are more concerned with accuracy-driven optimization than the concerns of safety and false negatives. In medical decision support systems, false negatives are more harmful.This paper presents a light-weight and interpretable machine learning approach for the early risk prediction of heart disease based on structured clinical data. Various models such as Logistic Regression, Random Forest, XGBoost, and stacking ensemble classifiers are compared based on clinically meaningful evaluation metrics such as accuracy, pre- cision, recall, F1-score, and ROC- AUC. The experimental results indicate that ensemble classifiers perform better than individual models, and the unoptimized StackingClassifier performs the best (Recall: 0.8807, F1-score: 0.8930, AUC: 0.9147). Cost-sensitive and threshold-optimized stacking further enhances the recall to 0.9266. To improve the transparency and clinical trust, SHAP and LIME are combined to offer global and local explanations. The findings point out ST depression, maximum heart rate reached, type of chest pain, cholesterol, and exercise-induced angina as the important risk factors. The proposed approach shows that simple and interpretable ensemble models can provide accurate heart disease risk predictions.
S. Shinde· International Journal of Bio...· 0 citations
Cardiovascular disease remains one of the leading causes of mortality worldwide, necessitating the development of accurate and interpretable predictive systems that support early diagnosis and clinical decision-making. While numerous machine learning models have demonstrated promising predictive capabilities, many operate as black-box systems that provide limited transparency regarding how predictions are generated. This lack of interpretability presents significant challenges in healthcare environments where trust, accountability, and regulatory compliance are essential. This study presents a comparative evaluation of six supervised machine learning classifiers for heart disease prediction using the Cleveland Heart Disease Dataset. The evaluated models include Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest Neighbours, Random Forest, and Gradient Boosting. A comprehensive machine learning pipeline comprising data preprocessing, feature selection, hyperparameter optimization, repeated stratified cross-validation, and performance evaluation was implemented. Explainable Artificial Intelligence (XAI) techniques based on SHAP were integrated to provide both global and local interpretability of model predictions. Experimental results demonstrate that the Decision Tree classifier achieved the highest overall performance, attaining an accuracy of 98.54%, precision of 98.21%, recall of 98.34%, and F1-score of 98.27%. Furthermore, SHAP-based analysis revealed that chest pain type, number of major vessels, exercise-induced angina, maximum heart rate, and ST depression were the most influential predictors of cardiovascular disease risk. The findings indicate that interpretable machine learning models can achieve predictive performance comparable to or exceeding more complex algorithms while maintaining transparency and clinical usability. The study contributes a reproducible framework for explainable cardiovascular disease prediction and demonstrates the feasibility of integrating interpretable machine learning models into clinical decision support systems. The proposed approach offers a foundation for trustworthy healthcare artificial intelligence applications that balance predictive accuracy with explainability.
Asoshi Paul Anule, Anagu Emmanuel John, Ogar Michael Oko· Middle East Journal of Appli...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
