Classification of Dementia using Machine Learning models and interpreting the results using LIME and SHAP

This work investigates the use of machine learning (ML) to the categorization of dementia using datasets. Transparency is improved by the development of strong machine learning models and interpretability strategies (LIME, SHAP). Handling missing values, standardizing features, and feature engineering (dimensionality reduction) are all part of comprehensive data preprocessing. Performance metrics evaluates models, and interpretability results highlight important features. The results highlight the value of interpretability and the effectiveness of machine learning. Strengths, weaknesses, biases, ethics, and practical difficulties are all discussed. Prospective paths prioritize ongoing enhancement and investigation of sophisticated machine-learning methods for dementia identification.