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Medical image data classification could be a prime data mining problem being discussed concerning for a decade that has attracted many researchers around the world. Most classifiers are designed therefore on learn from the info itself using a training method, as a result of complete expert information to work out classifier parameters is impossible. This paper proposes a FTD tree supported machine learning paradigm. As we tend to are using the data of the area masters for Alzheimer's disease, we have created one among the accommodating reasonable digression choice trees rule, wont to anticipate the sickness of the AD and upheld the physic chemical parameters The anticipated procedure of foreseeing the medical issue comprises of three imperative advances like vulnerability dealing with, include choice abuse lessen and center investigation, arrangement misuse the practical digression choice trees. The anticipated accommodating digression choice tree is developed by using a capacity called practical digression entropy for the choice of characteristics and split focuses.

Prediction and Classification of AD by using FTD Tree