Classification of Alzheimer’s Disease by Using FTD Tree
Due to the increasing demand on Alzheimer’s, the continuous monitoring of health and characteristics is significant for maximizing the yields. Even though many physicochemical parameters are available for monitoring the Alzheimer’s, the knowledge of domain experts is expected to analyze these parameters to find the final decision about the Alzheimer’s disease. In order to utilize the knowledge of the domain experts for Alzheimer’s disease, we have developed a functional tangent decision tree algorithm which predict the disease based on the physiochemical parameters. The proposed method of predicting the disease consists of three important steps such as uncertainty handling, feature selection using reduce and core analysis, classification using the functional tangent decision tree. The proposed functional tangent decision tree is constructed by utilizing a function called functional tangent entropy for the selection of attributes and split points.
KeywordsAlzheimer’s disease Decision tree Entropy Accuracy
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