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 performedaccurately and efficiently and its automation would be very useful. The patient’s records are collected fromNational Institute on Aging, USA. The Sample consisted of initial visits of 496 subjects seen either as controlor as patients. Patients were concerned about their memory at the National Institute on Aging. It alsoconsisted of patients and caregiver interviews. This research work presents different models for theclassification of different stages of Alzheimer’s disease using various machine learning methods such asNeural Networks, Multilayer Perceptron, Bagging, Decision tree, CANFIS and Genetic algorithms. Theclassification accuracy for CANFIS was found to be 99.55% which was found to be better when compared toother classification methods. Based on the outcome of classification accuracies, various management andtreatment 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 indiagnosis and treatment of AD.