Using CatBoost and Other Supervised Machine Learning Algorithms to Predict Alzheimer's Disease

Abstract

Alzheimer's disease is a progressive neurologic disorder that affects millions of elderly people worldwide. Most affected patients are not formally diagnosed due to the complexity of the disease and the lack of definitive diagnostic tools. Machine learning algorithms are powerful in deciphering complex data patterns. This study applied and evaluated a comprehensive set of nine machine learning techniques in detecting Alzheimer's disease. The model training and testing utilized clinical and brain magnetic resonance imaging features from The Open Access Series of Imaging Studies (OASIS) of Alzheimer's disease. The input data include ordinal data such as cognitive scores and numerical data of imaging measurements. To predict Alzheimer's disease, multiple types of supervised machine learning algorithms were trained, including CatBoost, logistic regression, decision tree, random forest, Naive Bayes, SVM, gradient boosting, XGBoost, and AdaBoost. A set of model performance metrics demonstrated that most algorithms were able to perform very well with high accuracy (92-96% in a longitudinal dataset). The models using CatBoost, SVM and decision tree performed the best. The results of this study suggest that ML algorithms combining clinical cognitive assessment and brain MRI images can assist and improve Alzheimer's disease diagnosis.