K-Nearest Neighbors for Cardiac Catheterization (Al-Farooq Journal of Sciences, 2026)
Cardiovascular disease (CVD) is the term for conditions affecting the heart and blood arteries. These conditions have been linked to the risk of heart attacks, strokes, and angina. Globally, CVDs are the primary cause of illness and mortality. Cardiac catheterization is a diagnostic and treatment tool for CVDs. The modeling of cardiac catheterization for binary classification can benefit from ML. This study looks into the classification of patients who require monitoring using K-Nearest Neighbors. utilizing a dataset of roughly 814 individuals that was acquired from the Benghazi Heart Disease Center. The research’s objective is to build a binary classifier that can predict whether or not a patient needs to be monitored by a physician following a catheterization operation. We discover that, with only minor influences on recall, the K-Nearest Neighbors model achieves an accuracy of 87.09% in identifying the critical patients
Keywords: Machine Learning (ML), Cardiovascular Disease (CVD), K-Nearest Neighbors (KNN).