Alzheimer Detection System

Abstract

Alzheimer’s disease affects the ageing population [1]. The risk of getting Alzheimer disease increases as one’s age increases. Worldwide, the percentage of people who have Alzheimer disease is 10% in over 65 years of age [2], 20% in over 80 years, and over 40% of people in over 90 years [4]. It is estimated that worldwide, more than 46 million people are suffering from Alzheimer disease, and this number is likely to increase to 131.5 million by 2050, since the life expectancy increases [3]. Earlier detection of Alzheimer’s disease can help with proper treatment and prevent brain tissue damage. With innovation and improvement in data-ware housing, data mining, machine learning and emergence of data science as an effective field of utilizing data as a powerful tool to predict useful information, many studies are being conducted to make this process affective. In this study Random Forest Classifier will be applied on the health parameters associated with Alzheimer disease to extract hidden patterns on which identification will be done. Alzheimer Detection System (ADS) would be developed to identify Alzheimer before time on the basics of identified attributes and algorithm. Hence precautionary measures would be taken in time. These precautionary measures will help to decrease the death rate caused by Alzheimer