Citation

Kumkum, Ananna Rashid and Sen, Anik and Noman, Abdullah Yousuf and Majumder, Prajukta and Liew, Tze Hui and Fahad, Nafiz and Miah, Md. Saef Ullah and Rabbi, Riadul Islam and Hossen, Md. Jakir (2025) From Data to Diagnosis: Applying Machine Learning Model for Reliable Heart Disease Prediction. In: 2025 5th Asia Conference on Information Engineering (ACIE), 10-12 January 2025, Phuket, Thailand.

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Abstract

Heart disease remains a significant global health challenge, demanding precise and interpretable predictive tools to aid early intervention and improve patient outcomes. This study applies the XGBoost machine learning algorithm to develop a robust model for predicting cardiovascular disease risk. Leveraging a comprehensive dataset that includes demographic, lifestyle, and clinical features, our approach combines extensive data preprocessing, feature engineering, and Bayesian hyperparameter tuning to achieve superior predictive accuracy, with an AUC of 0.98 and an accuracy of 97.48%. Key predictive factors, identified through SHAP (SHapley Additive exPlanations) values, include cholesterol, blood pressure, and physical activity, offering actionable insights for clinicians. The model’s interpretability supports its potential as a reliable, real-time diagnostic tool, enhancing clinical decision-making and risk assessment. This research advances predictive healthcare analytics by showcasing the effectiveness of sophisticated machine learning methods, including XGBoost, for precise and interpretable heart disease forecasting. Subsequent study will seek to validate the model across varied populations and incorporate it into clinical environments for adaptive, individualized cardiovascular risk management.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Cardiovascular diseases, XGBoost, Machine Learning, SHAP values, Risk Prediction
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	30 May 2025 01:10
Last Modified:	03 Jun 2025 08:09
URII:	http://shdl.mmu.edu.my/id/eprint/13874

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