<![CDATA[Heart disease remains one of the leading causes of death worldwide, making early detection and accurate classification essential to mitigating greater health risks. This study presents a comparative analysis of two machine learning algorithms Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM) for heart disease classification. The dataset used is the UCI Heart Disease dataset, comprising 920 patient records. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the SVM model achieved the highest accuracy at 84%, along with an F1-score of 86% for the positive class and a recall of 92%. In contrast, LightGBM demonstrated balanced performance, achieving 83% accuracy and an F1-score of 85%. These findings suggest that SVM holds a slight advantage in identifying heart disease cases, particularly in minimizing false negatives an aspect that is critical yet often overlooked in prior comparative studies.]]>
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