Analisis Komparatif Genetic Algorithm dan Particle Swarm Optimization dalam Optimasi Ensemble Learning untuk Prediksi Coronary Artery Disease

Rizal Wahyu Pratama, Khulika Malkan, Mikhael Setia Budi, Diah Septiani

Abstract


Coronary Artery Disease (CAD) merupakan penyebab utama kematian kardiovaskular di seluruh dunia dan proses diagnosisnya masih sangat bergantung pada prosedur invasif yang tidak selalu mudah diakses oleh semua pasien. Penelitian ini membandingkan kinerja Genetic Algorithm (GA) dan Particle Swarm Optimization (PSO) dalam melakukan seleksi fitur sekaligus optimasi hyperparameter secara simultan pada empat model ensemble learning untuk prediksi CAD menggunakan dataset Z-Alizadeh Sani yang memuat 303 data pasien dengan 56 atribut klinis. Kedua algoritma dikonfigurasi secara setara menggunakan representasi solusi berdimensi 43 dengan fungsi fitness berbasis F1-score dan mekanisme early stopping. Hasil penelitian menunjukkan bahwa model terbaik dicapai oleh kombinasi GA dengan Random Forest yang meraih F1-score sebesar 0,9438, akurasi 0,9180, dan ROC-AUC 0,9018. PSO Random Forest dan PSO LightGBM berhasil mencapai recall sempurna 1,0000 sehingga tidak ada satu pun pasien CAD yang terlewat dalam proses klasifikasi. Analisis interpretabilitas menggunakan SHAP mengungkap bahwa Nyeri Dada Tipikal, Usia, dan Fraksi Ejeksi adalah faktor klinis yang paling berpengaruh terhadap prediksi model, konsisten dengan konsensus kardiologi internasional.

Keywords


Coronary Artery Disease; Genetic Algorithm; Particle Swarm Optimization; Ensemble Learning; SHAP

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