Comparison of Data Mining Model Performance in Heart Disease Detection with Feature Selection Application
Perbandingan Kinerja Model Data Mining Dalam Deteksi Penyakit Jantung Dengan Penerapan Feature Selection
Abstract
Penyakit jantung merupakan penyebab utama kematian di seluruh dunia, sehingga deteksi dini sangat penting untuk meningkatkan harapan hidup pasien. Dengan kemajuan teknologi data mining dan machine learning, prediksi penyakit jantung dapat dilakukan lebih akurat. Penelitian ini membandingkan kinerja prediksi model Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), dan Support Vector Machine (SVM) dalam mendeteksi penyakit jantung menggunakan UCI Heart Disease Dataset. Teknik feature selection—Filter Method, Wrapper Method (RFE), dan Embedded Method—diterapkan untuk meningkatkan akurasi prediksi dan mengurangi kompleksitas model. Hasil eksperimen menunjukkan bahwa SVM mencapai akurasi tertinggi sebesar 91,2%, diikuti Random Forest dengan 90,7%. Penggunaan feature selection terbukti meningkatkan kinerja model secara signifikan dengan mengurangi dimensi data dan menghindari overfitting. Temuan ini menunjukkan efektivitas SVM dan Random Forest dalam pengembangan sistem prediksi penyakit jantung yang efisien di lingkungan klinis.
Kata kunci: Data Mining, Prediksi Penyakit Jantung, Feature Selection, Support Vector Machine
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