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
