Prediction and Influencing Factor Analysis of Alzheimer’S Disease Based on Machine Learning Algorithms

Current research on Alzheimer"s disease remains incomplete and faces significant challenges. This study aims to investigate the underlying causes of Alzheimer"s disease by leveraging machine learning algorithms and predictive models. The performance of three machine learning models including Random Forest (RF), XGBoost, and Support Vector Machine (SVM) were compared in predicting Alzheimer's disease. By employing grid search and five-fold cross-validation, we identified the optimal parameters for each model and constructed predictive models based on these parameters. For model evaluation, we used accuracy, recall, and the weighted average F1 score as metrics. The results demonstrated that the XGBoost model performed better than the others, achieving accuracy, recall, and F1 scores of 95.59%, 95.58%, and 95.56%, respectively. In contrast, the SVM model showed slightly lower performance, with all three metrics hovering around 89%. Therefore, the XGBoost model is considered the most suitable for Alzheimer's disease prediction. Additionally, the study identified key predictive features, including FunctionalAssessment, ADL, MMSE, MemoryComplaints, and BehavioralProblems. Future research could investigate ensemble learning techniques to further improve the model's predictive performance