KLASIFIKASI DATA SERANGAN JANTUNG MENGGUNAKAN METODE SUPER VECTOR MACHINE DAN ARTIFICIAL NEURAL NETWORK
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Abstract
This study aims to compare the performance of two machine learning algorithms, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN), in predicting the risk of heart attacks. The data used was obtained from Kaggle in 2021 and consists of 10 features, including parameters such as age, gender, and other relevant factors for predicting the risk of heart attacks. After the data cleaning and splitting process, both algorithms were trained and tested using accuracy, precision, recall, and F-1 score metrics. The research results show that SVM achieved an accuracy of 61%, while ANN achieved 84% accuracy. Based on these results, it can be concluded that ANN is more effective in predicting the risk of heart attacks from the dataset used. This study provides in-depth insights into the application of two machine learning algorithms, specifically SVM and ANN, in the context of predicting the risk of heart attacks. The findings may be beneficial for healthcare practitioners and researchers interested in developing predictive models for heart attack prevention.
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