Prediksi Kelangsungan Hidup Pasien Gagal Jantung Menggunakan Pendekatan Machine Learning dengan Optimasi GridSearchCV
DOI:
https://doi.org/10.33795/jip.v11i4.7938Keywords:
Gagal Jantung, Klasifikasi, Random Forest, Parameter Optimasi, Grid SearchCVAbstract
Gagal jantung menjadi salah satu penyebab utama tingginya angka kematian secara global, termasuk di Indonesia. Deteksi dini sangat krusial untuk mencegah progresivitas penyakit, tetapi pendekatan konvensional kerap memiliki keterbatasan akurasi. Penelitian ini memiliki tujuan untuk meningkatkan akurasi prediksi terhadap kelangsungan hidup pasien dengan kondisi gagal jantung melalui proses optimasi algoritma Machine Learning menggunakan teknik penyesuaian hiperparameter Grid SearchCV. Dataset yang digunakan berasal dari Heart Failure Clinical Records Dataset yang tersedia di UCI Machine Learning Repository, mencakup 299 data rekam medis pasien dengan 13 atribut klinis. Penelitian ini menggunakan enam algoritma klasifikasi, yang terdiri dari Random Forest, Decision Tree, Neural Network, K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), dan Naïve Bayes. Hasil evaluasi menunjukkan bahwa algoritma Random Forest menghasilkan akurasi tertinggi, yaitu sebesar 87%, sebelum dilakukan proses optimasi. Peningkatan performa dicapai dengan Grid SearchCV, menghasilkan akurasi akhir sebesar 95%. Temuan ini membuktikan bahwa optimasi Hyperparameter mampu meningkatkan kinerja model secara signifikan. Implementasi hasil penelitian dapat mendukung rumah sakit dan layanan kesehatan dalam meningkatkan ketepatan diagnosis dini serta pemantauan pasien. Selain itu, studi ini menjadi referensi pengembangan sistem prediksi medis berbasis Machine Learning yang lebih mutakhir di masa depan.
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