Dementia Identification for Diagnosing Alzheimer's Disease using XGBoost Algorithm

Abstract

Dementia is an aggregate term used to portray different side effects of psychological decay as oblivion. Around the globe, closely 50 million humans produce dementia, and there are very nearly 10 million fresh cases every year. The roadblock to the clinician is to determine the complex illness, for example, different kinds of dementia, Alzheimer's Disease, and Parkinson's Disease. Uncommonly, Alzheimer's disease is a bit complex to analyze as far as indications as they cover in numerous perspectives at the beginning phase. Along these lines, it is important to examine the cycle of analytic with more enhanced performance with various parameters of the disease. In this paper, we have classified dementia into three classes (AD Dementia, No Dementia, and Uncertain Dementia) for identifying Alzheimer's disease in its beginning phase using Extreme Gradient Boosting (XGBoost) algorithm and also shown the feature importance scores. We got an enhanced performance in terms of accuracy (81%), precision (85%), and other performance metrics, and “ageAtEntry” was the most important feature.