Published 2026-10-08
Keywords
- Array
Abstract
Cardiovascular diseases (CVDs) continue to be the leading cause of death worldwide, accounting for approximately 17.9 million deaths each year, which represents about 31% of global mortality. This study presents a comparative analysis of machine learning algorithms, specifically K-Nearest Neighbors (KNN) and Random Forest, for predicting cardiovascular events using clinical datasets. The research incorporates thorough data preprocessing and feature selection, emphasizing key clinical indicators and risk factors associated with heart disease. The performance of the models was evaluated using standard metrics, including accuracy, precision, recall, and F1-score. The findings indicate that the Random Forest model outperformed KNN, achieving an accuracy of 92%, while KNN recorded an accuracy of 82%. These results highlight the effectiveness of machine learning techniques in cardiovascular risk prediction, offering valuable support for early diagnosis, timely intervention, and improved patient outcomes.
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