Alzheimer Disease Prediction through Unsupervised Classification

Abstract

Alzheimer is considered to be more challenging to the researchers; it is complicated to predict the disease at the beginning stage and analyse it. But due to the availability of colossal brain image samples and neural dimensions, making a diagnosis is much more comfortable and practical. Researches show that machine learning technologies are more beneficial and useful in analysing this disease. The unsupervised classification, which is motivated by unsupervised learning, will help to learn raw data intelligently. This paper proposes unsupervised classification using k-means clustering, which gives a good result. The proposed work is analysed based on performance measures like Accuracy (ACC), Sensitivity (SCN), Specificity (SPE).