Weighted Randomn Forest Model for Significant Feature and Disease Progression Prediction

Abstract

Abstract In recent studies, several machine learning and deep learning prediction models have been proposed for the early detection and classification of various stages of Alzheimer's Disease (AD). Many years before the actual onset of AD, there occur several structural changes in the brain. These structural brain features can be utilized in learning the disease progression from early stage of disease. The various stages of pathology cause mild cognitive impairment (MCI) from their normal cognition and AD from normal cognition. This chapter intends to develop a random forest learning model that utilizes a relevant subset of predictors to diagnose the progression of the disease. The conversion from normal cognition to MCI is identified at an early stage of the onset of structural brain changes. The importance of existing research works lies in more early identification of significant feature that increases the disease progression and appropriate inventions greatly improves subjects' recovery. The ADNI cross-sectional MRI data were analysed in this study that utilized brain curvature, grey matter white matter density, volume of cortical and sub cortical structures, shape of hippocampus, hippocampal subfield volume, Mini-mental state exam (MMSE), Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume, Normalize Whole Brain Volume, and Atlas Scaling Factor for constructing randomized trees and thus predicting the features that cause the progression of disease stages from MCI to Alzheimer’s disease that causes dementia. This implementation model proved to give robust AD conversion probability and identifying significant features that are sufficient for future clinical inferences.