Analysis of Machine Learning Algorithms for Early Detection of Alzheimer’s Disease: A Comparative Study - Deni Gunawan; Robi Aziz Zuama; Muhamad Abdul Ghani (Yayasan Kita Menulis, 2024)
This study aims to analyze and compare the performance of various machine learning algorithms in predicting Alzheimer's disease based on patient clinical data. The algorithms tested include Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression. The dataset used in this research consists of clinical data from patients, encompassing various health parameters. The results indicate that the Decision Tree and Random Forest algorithms provide the best performance, with an overall accuracy of 93%. Random Forest performs slightly better in recall for class 0 but slightly worse in recall for class 1 compared to Decision Tree. Logistic Regression also shows good performance with an overall accuracy of 83%, while K-Nearest Neighbors has the lowest performance with an overall accuracy of 72%. This research offers insights into the effectiveness of various machine learning algorithms in detecting Alzheimer's disease and underscores the importance of selecting the appropriate model based on data characteristics and application needs. For future research, it is recommended to further optimize the model hyperparameters, increase the dataset size, add new relevant features, and combine several models using ensemble learning techniques. External validation and the development of more interpretable models are also crucial to build trust in the use of machine learning in the healthcare field.