There has been a steady rise in the number of patients suffering from Alzheimer’s disease (AD)
all over the world. Medical diagnosis is an important but complicated task that should be performed
accurately and efficiently and its automation would be very useful. The patient’s records are collected from
National Institute on Aging, USA. The Sample consisted of initial visits of 496 subjects seen either as control
or as patients. Patients were concerned about their memory at the National Institute on Aging. It also
consisted of patients and caregiver interviews. This research work presents different models for the
classification of different stages of Alzheimer’s disease using various machine learning methods such as
Neural Networks, Multilayer Perceptron, Bagging, Decision tree, CANFIS and Genetic algorithms. The
classification accuracy for CANFIS was found to be 99.55% which was found to be better when compared to
other classification methods. Based on the outcome of classification accuracies, various management and
treatment strategies such as pharmacotherapeutic and non pharmacotherapeutic interventions for mild,
moderate and severe AD were elucidated, which can be of enormous use for the medical professionals in
diagnosis and treatment of AD
