Early Detection of Alzheimer’s Disease Using Machine Learning Models with Explainable AI

Alzheimer’s disease significantly impacts cognitive function, making early detection crucial. This study evaluates the performance of five machine learning models—Random Forest, Decision Tree, Neural Networks, SVM, and KNN—using a dataset with features like Functional Assessment and Memory Complaints scores. Among these, the Random Forest Classifier with an 80:20 train-test ratio achieved the highest accuracy of 95.35%. Explainable AI techniques, specifically LIME, were used to interpret the model’s predictions, highlighting key features influencing the decision-making process. Our findings suggest that the Random Forest model offers a reliable, cost-effective approach to Alzheimer’s detection, potentially enhancing traditional diagnostic methods. Future work should focus on expanding the dataset, incorporating additional features, and validating the models in clinical settings to improve applicability and interoperability. disease.