A Survey of Machine Learning-Based Approaches for Alzheimer’s Disease Prediction

Abstract

MRI has been proven a key role player in the diagnosis of neurological diseases. It was found during the study that differentiation among various neurological disorders is not an easy task due to similarities in symptoms. Novel computation tools based on ML schemes are useful in knowing complex brain functions and diseases. This paper significantly examines and compares the performances of the many ML-based methods to detect neurological disorders—focusing on Alzheimer’s disease from MRI data. The development of a novel bioindicator is needed for the prompt diagnosis and prognosis of disorders. The key challenge in this area is to develop a generalized approach for clinical implementation on regular data. The article evaluates and compares the performances of machine - learning based techniques to predict Alzheimer’s disease from MRI data. Finally, future research directions are indicated.