Publication

Abstract : Dementia, a brain-related illness, is afflicting numerous individuals worldwide, and it is imperative to detect the disease at its early stages. Alzheimer's disease (AD), which affects approximately 60% of those with dementia, is one type of this condition. This project's primary objective is to use multilayer perceptron and maximal information coefficient for neuropsychological testing to diagnose Alzheimer's disease. In this research, the primary aim is to assess the efficacy of utilizing the maximal information coefficient (MIC) as a means to identify and diagnose Alzheimer's disease (AD) in its early stages. The study utilizes neuropsychological tests and data from magnetic resonance imaging (MRI) to investigate the relation between the MIC coefficients and the progression of AD. The results of the study suggest that MIC coefficient can be a reliable measure for detecting the beginning phases of AD. This discovery might open up a new path for the creation of diagnostic tools that can detect early-onset AD more accurately, which could ultimately lead to earlier interventions and improved patient outcomes.