Optimalisasi Machine Learning dengan Hyperparameter Tuning untuk Prediksi Cardiovascular Disease

• FADLAN HAMID ALFEBI
• 14210242

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ABSTRAK

ABSTRAK

Nama               : Fadlan Hamid Alfebi

NIM                  : 14210242

Program Studi : Ilmu Komputer

Fakultas           : Teknologi Informasi

Jenjang            : Strata Dua (S2)

Konsentrasi      : Data Mining

Judul        : “Optimalisasi Machine Learning dengan Hyperparameter Tuning untuk Prediksi Cardiovascular Disease”

Penyakit kardiovaskular (CVD) adalah penyebab utama kematian di seluruh dunia. Pencegahan primer dengan prediksi awal timbulnya penyakit. Menggunakan data laboratorium dari National Health and Nutrition Examination Survey (NHANES) pada jangka waktu 2017-2020 (N=8.544), kami mengoptimalisasi algoritma machine learning (ML) dengan hyperparameter tuning untuk mengklasifikasikan individu yang berisiko. Model ML dievaluasi berdasarkan kinerja klasifikasinya setelah dilakukan beberapa teknik data preprocessing, di antaranya feature selection, imputasi missing value, dan teknik resampling. Pada model ML dasar, Logistic Regression (LR) memiliki hasil terbaik dibanding model ML lain dengan akurasi sebesar 91.46% dan area under receiver operating characteristics (AUROC) sebesar 92.22%. Setelah diterapkan hyperparameter tuning HyperOpt, akurasi meningkat menjadi 92.98% dan AU-ROC menjadi 93.90%. Performa akhir dalam memprediksi CVD mengungguli studi sebelumnya.

KATA KUNCI

Klasifikasi,Cardiovascular Disease,machine learning,Logistic Regression,Hyperparameter Tuning

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DAFTAR PUSTAKA

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