Evaluasi dan Perbandingan Metode XGBoost dan LightGBM Dalam Deteksi Dini Penyakit Alzheimer - Muhammad Rezky Adytama; Egi Safitri; Asmaul Dwi Akbar; Nicholas Svensons; Raka Sebastian Musin (Journal of Data Science Methods and Applications, 2026)
Early detection of Alzheimer’s disease is a key step in slowing disease progression and improving patients’ quality of life. This study evaluates and compares the performance of the XGBoost and LightGBM algorithms in diagnosing Alzheimer’s disease, using a longitudinal dataset comprising 2.149 subjects. The dataset underwent meticulous preprocessing, including handling missing data, feature selection, and duplicate data removal, to ensure data reliability. Model evaluation was conducted based on accuracy, precision, recall, F1-score, and Mean Squared Error (MSE) metrics. The results demonstrate that the LightGBM algorithm outperforms XGBoost, achieving an accuracy of 87%, precision of 87%, recall of 85%, F1-score of 85%, and an MSE of 0.12. The advantages of LightGBM include computational efficiency and the ability to handle large-scale data, making it more effective than XGBoost. This study provides guidance on selecting the optimal algorithm for clinical applications, enabling more effective early interventions to mitigate the adverse impacts of Alzheimer’s disease.