Optimizing Alzheimer's disease prediction through ensemble learning and feature interpretability with SHAP-based feature analysis.

INTRODUCTION

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia. Early diagnosis is vital. We developed an interpretable machine learning (ML) model for early AD prediction using open clinical data.

METHODS

Data from 2149 adults (60-90 years) were obtained from Kaggle. After preprocessing and feature engineering, tree-based models were trained. A stacking ensemble model combining Gradient Boosting and XGBoost was trained, with Logistic Regression as the meta-learner. SHapley Additive exPlanations (SHAP) provided interpretability. Performance was measured by accuracy, precision, recall, F1 score, ROC and AUC.

RESULTS

The stacked ensemble achieved 97% accuracy (AUC 0.97), with 0.97 precision, 0.94 recall, and 0.96 F1 score for AD. SHAP identified memory complaints, Mini-Mental State Examination (MMSE), functional assessment, behavioral symptoms, cholesterol, and lifestyle factors (activity, diet, sleep) as top predictors.

CONCLUSION

The ensemble model, enhanced by SHAP analysis, provides accurate and interpretable AD risk predictions with potential applicability in future clinical decision support systems.

HIGHLIGHTS

Developed an ensemble machine learning (ML) model for early Alzheimer's disease (AD) prediction.Achieved 97% accuracy using stacked XGBoost and Gradient Boosting.SHapley Additive exPlanations (SHAP) analysis identified key cognitive and lifestyle-related risk factors.Model interprets AD risk using explainable artificial intelligence (AI) for clinical applicability.Utilized open-access dataset to ensure reproducibility and transparency.