Advanced Machine Learning Implementation For Early Detection and Prediction of Alzheimer's Disease

Silalahi, Christian Petrus and Lestari, Sri (2025) Advanced Machine Learning Implementation For Early Detection and Prediction of Alzheimer's Disease. Advanced Machine Learning Implementation For Early Detection and Prediction of Alzheimer's Disease, 5 (1). pp. 1-11. ISSN 2614-3372

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Abstract

Early detection of Alzheimer's disease is essential for more effective patient care. This study explores the application of machine learning algorithms in detecting Alzheimer's disease by analyzing influential factors, such as demographic profile, medical history, and clinical examination results. Five machine learning methods, namely Deep Learning, Random Forest, Decision Tree, Naïve Bayes, and Logistic Regression, are used to classify Alzheimer's disease cases. In addition, the study used RFE and BPSO methods for feature selection with the aim of improving model performance. Evaluation is conducted using Cross-Fold Validation and Split Validation. The result of the study shows that the Random Forest+BPSO algorithm outperforms the other algorithms, as shown by the prediction accuracy value of 99.1%, followed by the Random Forest+RFE algorithm with an accuracy of 98.3%, and Decision Tree+PSO with an accuracy of 98.2%.

Item Type:	Article
Subjects:	Ilmu Komputer eSkripsi
Divisions:	Skripsi/TA & PKPM/KP - Fakultas Ilmu Komputer > Teknik Komputer
Depositing User:	Christian Petrus Silalahi
Date Deposited:	08 Dec 2025 04:33
Last Modified:	08 Dec 2025 04:33
URI:	http://repo.darmajaya.ac.id/id/eprint/23353

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