• Shaik Javeed
• Mrs V Dakshayani

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that impairs memory, cognition, and behavior. Early diagnosis is vital for timely treatment, yet traditional clinical and neuroimaging methods are often costly and time-consuming. This study proposes an explainable machine learning framework using the Extreme Gradient Boosting (XGBoost) algorithm for accurate and interpretable AD prediction. The dataset includes demographic, lifestyle, medical, and cognitive features such as age, gender, BMI, cholesterol, blood pressure, MMSE score, and behavioral symptoms. Exploratory data analysis and visualizations were performed to identify key patterns and correlations. The XGBoost model achieved high accuracy, precision, recall, F1-score, and ROC-AUC in distinguishing healthy and AD subjects. SHAP (SHapley Additive exPlanations) analysis provided interpretability by identifying the most influential features. The proposed XGBoost–SHAP framework ensures reliable early AD detection and supports personalized clinical decisions.

ALZHEIMER’S DISEASE PREDICTION USING XGBOOST MACHINE LEARNING MODEL WITH SHAP EXPLAINABILITY

"ALZHEIMER’S DISEASE PREDICTION USING XGBOOST MACHINE LEARNING MODEL WITH SHAP EXPLAINABILITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.a266-a275, November-2025, Available :http://www.jetir.org/papers/JETIR2511033.pdf

"ALZHEIMER’S DISEASE PREDICTION USING XGBOOST MACHINE LEARNING MODEL WITH SHAP EXPLAINABILITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppa266-a275, November-2025, Available at : http://www.jetir.org/papers/JETIR2511033.pdf