A Lightweight Cost-Sensitive Explainable Ensemble Framework for Early Heart Disease Risk Prediction

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 02 | Page :
    By

    Satyam Shinde,

  • Mr. Nikhilesh Mankar,

  • Amrut Nikam,

  • Dr. Swati Shirke,

  • Dr. Rahul Sonkambale,

  1. Research scholar, CSE, Pimpri Chinchwad University, India
  2. Professor, CSE, Pimpri Chinchwad University, India
  3. Research scholar, CSE, Pimpri Chinchwad University, India
  4. professor, CSE-AIML, Pimpri Chinchwad University, India
  5. Professor, CSE-AIML, Pimpri Chinchwad University, India

Abstract

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.

Keywords: Heart Disease Prediction, Explainable Artificial Intelligence, Ensemble Learning, Stacking Classifier, Cost-Sensitive Learning, Threshold Optimization, SHAP, LIME.

How to cite this article:
Satyam Shinde, Mr. Nikhilesh Mankar, Amrut Nikam, Dr. Swati Shirke, Dr. Rahul Sonkambale. A Lightweight Cost-Sensitive Explainable Ensemble Framework for Early Heart Disease Risk Prediction. International Journal of Bioinformatics and Computational Biology. 2026; 04(02):-.
How to cite this URL:
Satyam Shinde, Mr. Nikhilesh Mankar, Amrut Nikam, Dr. Swati Shirke, Dr. Rahul Sonkambale. A Lightweight Cost-Sensitive Explainable Ensemble Framework for Early Heart Disease Risk Prediction. International Journal of Bioinformatics and Computational Biology. 2026; 04(02):-. Available from: https://journals.stmjournals.com/ijbcb/article=2026/view=246467


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Ahead of Print Subscription Review Article
Volume 04
02
Received 11/05/2026
Accepted 06/06/2026
Published 06/06/2026
Publication Time 26 Days
15

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