Peningkatan Akurasi Deteksi Dini Penyakit Parkinson melalui Pendekatan Ensemble Learning dan Seleksi Fitur Optimal - Wulandari, Kang Andini; Nugraha, Adhitya; Luthfiarta, Ardytha; Nisa, Laila Rahmatin (Universitas Hamzanwadi, 2024)
Early detection of Parkinson's disease (PD) is essential to enhance patient quality of life through timely intervention. This research aims to develop a predictive model using an ensemble learning approach and optimal feature selection. This experimental study employs three machine learning algorithms: random forest, XGBoost, and extra trees, optimized through hyperparameter tuning, feature selection techniques, and Kernel Principal Component Analysis (KPCA) for dimensionality reduction. The study utilizes the UCI Machine Learning Parkinson Dataset, which consists of 80 samples and 44 acoustic features extracted from patients' voices as they sustain the vowel sound