Cardiovascular Disease Risk Classification Using Machine Learning with Weighted Feature Fusion and Explainable AI on Bangladeshi Clinical and Lifestyle Data

Authors

Tasnimul Intazam Asif
International University of Business Agriculture and Technology (IUBAT)
Bishwaprotap Ray
International University of Business Agriculture and Technology (IUBAT)
Md. Alomgir Hossain
International University of Business Agriculture and Technology (IUBAT)
Faisal Imran
Department of Computer Science and Engineering (CSE) & CSIT, Shanto-Mariam University of Creative Technology, Dhaka, Bangladesh
Prime Biswajit Barua
Department of Computer Science and Engineering, International University of Business and Agricultural Technology
Nishat Salsabil Anisha
International University of Business Agriculture and Technology (IUBAT)
Ariful Haque Minhaj
International University of Business Agriculture and Technology (IUBAT)
Amit Roy
International University of Business Agriculture and Technology (IUBAT)
DOI: https://doi.org/10.52549/ijeei.v14i1.7421
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Published: 17-02-2026 • Issue: Vol. 14 No. 1: March 2026 • Section: Regular paper

Abstract

Cardiovascular disease (CVD) is one of the top causes of death across the world, and there is a need to develop early risk prediction models that can be accurate and interpreted. This study introduces a weighted feature fusion (WFF) model of machine learning to integrate clinical, lifestyle, and engineered features into an integrated machine learning model to improve the classification of CVD risk and the interpretability of the model. Several classifiers, such as the Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), Bagging, Decision Tree were trained and tested based on fusion-based methods. The experimental findings indicated that the highest classification accuracy of the model at 91% obtained by the Random Forest model. Moreover, the model was better in terms of discrimination as ROCAUC scores were over 0.980447in all categories of CVD risk. Explainable AI algorithms, such as SHAP and LIME were used to provide transparency, when combined with feature fusion, leads to a significant improvement in accuracy, reliability, and interpretability of CVD risk prediction models that can lead to the development of data-driven healthcare decision support systems of trust.
Keywords: Cardiovascular Disease; Weighted Feature Fusion; Explainable AI; SHAP; LIME; Random Forest; Risk Prediction

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Author Biographies

Tasnimul Intazam Asif
Department of Computer Science and Engineering, Student
Bishwaprotap Ray
Department of Computer Science and Engineering, Student
Md. Alomgir Hossain
Department of Computer Science and Engineering, Associate Professor
Nishat Salsabil Anisha
Department of Computer Science and Engineering, Student
Ariful Haque Minhaj
Department of Computer Science and Engineering, Student
Amit Roy
Department of Computer Science and Engineering, Student

How to Cite

[1]
T. I. Asif et al., “Cardiovascular Disease Risk Classification Using Machine Learning with Weighted Feature Fusion and Explainable AI on Bangladeshi Clinical and Lifestyle Data”, IJEEI, vol. 14, no. 1, pp. 44–63, Feb. 2026, doi: 10.52549/ijeei.v14i1.7421.