<![CDATA[Heart disease remains the leading cause of mortality globally, despite having significant potential to be controlled through early detection and effective risk-factor management. To improve the accuracy and efficiency of early detection, machine learning technology is employed to develop predictive models for heart disease risk. The research aims to compare the performance of three classification algorithms in predicting heart disease risk to identify the most optimal model. This research applies the CRISP-DM methodology to build and compare predictive models for heart disease risk using three supervised learning algorithms: K-Nearest Neighbors (K-NN), Naïve Bayes, and Logistic Regression. The dataset used is a heart disease dataset obtained from the Kaggle platform, consisting of 10,000 records with variables such as Age, Blood Pressure, Smoking, Diabetes, Cholesterol, Triglyceride Level, Fasting Blood Sugar, and CRP Level. For the K-NN model, experiments were conducted using three values of k (k = 5, k = 10, and k = 20) to examine the effect of the number of neighbors on model performance. Meanwhile, the Naïve Bayes and Logistic Regression models were implemented using default parameters without additional tuning to ensure a consistent performance comparison. Model performance was evaluated using Accuracy and F1-Score metrics. The evaluation results indicate that the K-NN model with k = 5 achieved the best performance, with an accuracy of 0.7203 and an F1-Score of 0.7598, outperforming the Naïve Bayes and Logistic Regression models.]]>
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