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Bayesian Neural Network untuk Prediksi Diabetes: Uncertainty Quantification dalam Machine Learning
Abstract
Penelitian ini bertujuan mengevaluasi dan membandingkan kinerja tiga model machine learning, yaitu random forest (RF), feedforward neural network (FNN), dan bayesian neural network (BNN), dalam klasifikasi diabetes menggunakan Diabetes Health Indicators Dataset dari UCI Machine Learning Repository yang memiliki ketidakseimbangan kelas. Prapemrosesan data meliputi normalisasi fitur menggunakan StandardScaler dan penanganan ketidakseimbangan kelas dengan synthetic minority over-sampling technique (SMOTE). Evaluasi model dilakukan menggunakan metrik akurasi dan skor F1, yang didukung oleh classification report dan confusion matrix. Hasil evaluasi menunjukkan bahwa RF menghasilkan akurasi tinggi (0,8493) namun skor F1 yang rendah (0,3386), yang mengindikasikan rendahnya sensitivitas model terhadap kasus positif diabetes. FNN memberikan performa yang lebih seimbang dengan skor F1 sebesar 0,4490 setelah penyesuaian threshold optimal. Sementara itu, BNN mencapai akurasi 0,8498 dan skor F1 sebesar 0,4043, serta memiliki keunggulan tambahan berupa kemampuan mengukur ketidakpastian prediksi melalui pendekatan Monte Carlo Dropout. Dengan demikian, FNN lebih unggul dalam keseimbangan klasifikasi, sementara BNN lebih relevan untuk aplikasi medis yang membutuhkan informasi tingkat kepercayaan prediksi guna mendukung pengambilan keputusan klinis yang lebih andal.
This study aims to evaluate and compare the performance of three machine learning models, namely random forest (RF), feedforward neural network (FNN), and bayesian neural network (BNN), for diabetes classification using the Diabetes Health Indicators Dataset from the UCI Machine Learning Repository, which exhibits significant class imbalance. Data preprocessing includes feature normalization using StandardScaler and class imbalance handling through synthetic minority over-sampling technique (SMOTE). Model performance is evaluated using accuracy and F1-score metrics, supported by classification report and confusion matrix analysis. The results show that RF achieves high accuracy (0.8493) but a low F1-score (0.3386), indicating poor sensitivity to positive diabetes cases. FNN provides more balanced performance with an F1-score of 0.4490 after optimal threshold adjustment. Meanwhile, BNN achieves an accuracy of 0.8498 and F1-score of 0.4043, while offering the additional advantage of uncertainty quantification through Monte Carlo Dropout. Therefore, FNN is more effective for balanced classification performance, while BNN is more suitable for medical applications that require prediction confidence information to support more reliable and informed clinical decision-making.
Kata Kunci: Prediksi diabetes, kuantifikasi ketidakpastian, bayesian neural network, classification imbalance, machine learning.
Keywords: Diabetes prediction, uncertainty quantification, bayesian neural network, classification imbalance, machine learning.
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