What type of study is this?

This is a Quantitative Study study.

September 18, 2025

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

Key Points

The optimized XGBoost model achieved 94.7% accuracy, enhancing early detection of heart disease.
Key predictors include chest pain category, maximum heart rate, and exercise-induced ST depression.
This method effectively combines support vector machines, random forests, and XGBoost for risk estimation.
The framework offers a solid balance between accurate predictions and interpretability for clinicians.

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.

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