Prediction of Alzheimer’s disease (AD) Using Machine Learning Techniques with Boruta Algorithm as Feature Selection Method

Abstract

Abstract Alzheimer’s disease (AD) is the most frequent incurable neurodegenerative disease, a general term for memory loss and other cognitive abilities. Early detection of AD can help with proper treatment and prevent brain tissue damage. Traditional medical tests are time consuming, fail to recognize early signs and lack of diagnosis sensitivity and specificity. To achieve promising prediction accuracy, the best predictive machine learning model is selected based on initial pre-processing step followed by vital attributes selection and performance evaluation for five proposed supervised machine learning algorithms. In the pre-processing, all the missing values have been removed since the overall percentage only covered 5.63%. Boruta algorithm as feature selection method resulted Atlas Scaling Factor, Estimated Total Intracranial Volume, Normalized Whole-brain Volume, Mini-Mental State Examination and Clinical Dementia Rating must be included as primary features. With Boruta algorithm, it has been shown that Random Forest Grid Search Cross Validation (RF GSCV) outperformed with 94.39% of accuracy, 88.24% sensitivity, 100.00% specificity and 94.44% AUC among other 12 models that includes conventional and fine-tuned models even for the small OASIS-2 longitudinal MRI dataset. Finally, our developed Graphical User Interface (GUI) prediction tool was evaluated through prediction over OASIS-1 cross-sectional MRI dataset containing 216 samples of imaging sessions that have been pre-processed. Prediction results were closed with the dementia status provided in OASIS cross-sectional data fact sheet.