Early Detection of Alzheimer’s Disease Using Machine Learning, Feature Selection, Data Balancing, and Explainable AI

Alzheimer’s disease is a progressive neurodegenerative disorder that causes cognitive decline and affects millions of people worldwide. Early detection is essential for timely intervention, yet conventional diagnostic methods often identify the disease at later stages, limiting treatment options. This study explores the application of machine learning models for the early prediction of Alzheimer’s disease using a dataset comprising demographic, health, lifestyle, and clinical information. To enhance predictive performance, feature selection techniques were employed to identify the most relevant factors, and the synthetic minority oversampling technique was used to address class imbalance. Model explainability was ensured through SHapley Additive exPlanations, providing insights into feature contributions. Among the tested machine learning models, CatBoost demonstrated superior performance, achieving an accuracy of 95% in predicting early-stage Alzheimer’s disease. These findings underscore the potential of artificial intelligence in aiding early diagnosis, which could lead to timely medical interventions and improved patient outcomes. This study highlights the importance of integrating AI-driven approaches in healthcare to optimize disease prediction and management.