Alzheimer's Disease Analysis Using Machine Learning Approach

Alzheimer's disease is a progressive neurological disorder marked by memory loss, cognitive decline, and functional impairment, which can hinder daily activities and independence. Early diagnosis of Alzheimer's disease is crucial for timely intervention and improved outcomes for patients. In this paper, several supervised ML algorithms are employed for Alzheimer's disease stage classification using a clinical dataset of 2,149 records that includes cognitive, behavioral, and lifestyle variables. The dataset undergoes preprocessing through normalization, encoding, and feature selection methods. The models are trained, evaluated, and the results are reported. Logistic_Regression, Random_Forest, and XGBoost models are trained, their performances are compared by accuracy, precision, recall, F1-score, and ROC-AUC metrics, and XGBoost was found to have the best accuracy at 96.05 %, closely followed by Random_Forest. A feature correlation matrix is calculated to assess the multicollinearity and redundancy of the dataset features. Furthermore, a confusion matrix is used to evaluate the model's performance on class-specific predictions. In addition to prediction accuracy, the models' explainability is also explored using SHAP and LIME techniques. These methods reveal the impact of specific features such as MMSE scores, memory loss symptoms, and lifestyle habits on the models' decisions. This information can help provide insights to clinical decision-makers and build a bridge between the black-box models and clinical practice. This study showed that high-performing and interpretable ML models can be used to build an automatic or assistive system for Alzheimer's disease diagnosis and integrate it into healthcare. The paper discusses its contributions to the field of explainable artificial intelligence (XAI).