MODEL PREDIKSI PENYAKIT JANTUNG MENGGUNKAN MACHINE LEARNING
DOI:
https://doi.org/10.36050/46ccvp37Keywords:
Penyakit Jantung, Seleksi Fitur, PSO, Machine LearningAbstract
Penyakit jantung merupakan salah satu penyakit yang menjadi penyebab utama kematian diseluruh dunia. Deteksi dini menjadi sangat penting supaya dapat membantu mengurangi risiko kematian serta mencegah komplikasi yang lebih parah dengan memungkinkan penanganan yang lebih cepat dan tepat. Dalam konteks ini, penerapan teknologi machine learning (ML) di bidang medis memberikan potensi besar untuk meningkatkan akurasi diagnosis dan prediksi penyakit jantung. Dalam penelitian ini, peneliti bertujuan untuk mendapatkan model ML yang dapat memprediksi penyakit jantung dengan performa terbaik menggunakan dataset penyakit jantung Cleveland UCI. Untuk meningkatkan performa dari model, peneliti juga menggunakan algoritma particle swarm optimaziation (PSO) untuk mengurangi jumlah fitur dalam dataset agar dapat mempunyai dampak yang signifikan terhadap kinerja model ML. Model ML dilatih dengan dataset yang telah dipilih fiturnya dengan algoritma PSO kemudian diuji, dan kinerjanya dibandingkan. Performa tertinggi diperoleh untuk model klasifikasi XGBoost yang dilatih pada dataset dengan algoritma PSO, dengan akurasi sebesar 86%, precision 86%, recall 86%, dan f1-score 86%. Hasil penelitian menunjukkan bahwa kombinasi algoritma PSO dan XGBoost memiliki kinerja yang paling baik untuk digunakan dalam prediksi penyakit jantung.
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