Developed a machine learning classification model to predict Alzheimer’s disease diagnosis using ...Developed a machine learning classification model to predict Alzheimer’s disease diagnosis using ...
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Kevin Williams
• Sep 24
Developed a machine learning classification model to predict Alzheimer’s disease diagnosis using clinical, cognitive, behavioral, and lifestyle data. The project included exploratory data analysis, feature engineering, class-imbalance handling, model training, and performance evaluation.
The final XGBoost model achieved 94.4% test accuracy, with 0.92 precision, recall, and F1-score for the positive diagnosis class. Feature-importance analysis identified memory complaints, behavioral problems, functional assessment, activities of daily living (ADL), and MMSE scores among the strongest predictive features.
The project demonstrates an end-to-end machine learning workflow—from understanding relationships within the data to evaluating and interpreting a predictive model.
Completed work
XGBoostAI Model DevelopmentData AnalysisData ScienceJupyterPython
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