ENHANCED PREDICTIVE MODELING FOR ALZHEIMER'S DISEASE: INTEGRATING CLUSTER-BASED BOOSTING AND GRADIENT TECHNIQUES WITH OPTIMIZED FEATURE SELECTION

The early signs of Alzheimer’s disease (AD) prove difficult to detect since this progressive illness has an ongoing character. The correct prediction of how the disease progresses represents a critical need for providing prompt treatment that enables effective management of the disease. The research develops Cluster-Integrated Boosting and Gradient (CIBG) as a stacked ensemble which combines CatBoost with Gradient Boosting Machine (GBM) classifiers to boost AD diagnostic precision. Kaggle provided the dataset containing diverse health-related characteristics together with lifestyle characteristics and cognitive data. The data preprocessing steps included handling missing values and normalization procedures coupled with SMOTE implementation to solve class imbalance problems. The feature selection process involved using Recursive Feature Elimination (RFE) which determined the most important predictive variables. The CIBG model uses CatBoost’s ordered boosting method to manage categorical features together with GBM’s gradient boosting structure that performs classification. Within CIBG the base classifier chooses logistic regression. Model performance evaluation included accuracy together with precision, recall, and F1-score metrics which reached an optimal rate of 96.8%. The CIBG approach produces results that show better performance than standard classifiers while maintaining stronger robustness and improved interpretability capabilities. The research shows how hybrid machine learning methods help enhance early diagnosis of AD and develops medical systems that are dependable for clinical use.