Optimalisasi Model Logistic Regression untuk Prediksi Diabetes Menggunakan Seleksi Fitur Berbasis Korelasi - Nugraha, Wahyu; Syarif, Muhamad (LPPM STMIK IKMI Cirebon, 2025)
Diabetes Mellitus is a pressing global health challenge, making early detection a key component of effective intervention. Machine learning has shown great potential in predicting diabetes risk. Among various models, Logistic Regression (LR) is often favored in a medical context due to its high interpretability, although its accuracy frequently lags behind more complex black-box models. LR performance is known to be highly sensitive to the quality and relevance of input features. This study aims to quantitatively evaluate the impact of a strict correlation-based feature selection strategy on the accuracy of the Logistic Regression model. Using the