AI-Driven Early Detection of Heart Disease Through Machine Learning and Clinical Metrics

Author(s)	Ms. Sarika Ganpatrao Shinde
Country	India
Abstract	Prompt identification of cardiovascular disease can greatly reduce global mortality. We propose a comprehensive AI pipeline that combines Support Vector Machines, Random Forests, and XGBoost to estimate heart disease risk using 13 routine clinical features. Evaluating a cohort of 303 patients, our optimized XGBoost model achieved 94.7% accuracy, 95.2% recall, and 94.1% specificity. Explainability analyses highlight chest pain category, maximum heart rate, and exercise-induced ST depression as the top predictors. This framework offers both robust performance and clear interpretability, empowering clinicians with actionable insights.
Keywords	Heart disease, Machine learning, Early detection, XGBoost, Clinical data, AI interpretability
Field	Computer Applications
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-09-17
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.55976

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