Özet:

In the case of too many variables, manual classification becomes a time- and labor-intensive process. For instance, classifying people with disabilities according to the problems they experience in self-care activities is a time-consuming process for specialist therapists. The aim of this study is to develop a model that can predict the problems experienced by physically and motor disabled individuals in self-care activities in order to reduce time spent by specialist therapist. Seven different data mining algorithms (support vector machines, artificial neural networks, C5.0, CART, QUEST, CHAID, bayesian networks) have been used in the estimation process. These algorithms have been applied individually and by forming different ensemble models. When experimental results of single and ensemble models were compared, it was seen that ensemble learning technique combined with two or more algorithms increased predictive performance.

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