Abstract
The ultimate aim of the proposed method is to establish a model for classification of medical data. Various methods have been generated to health related data to detect upcoming health fitness usage including detecting person's spending and illness related issues for diseased persons. In order to achieve promising results in medical data classification, we have planned to utilise orthogonal local preserving projection and optimal classifier. Initially, the pre-processing will be applied for extracting useful information and to convert suitable sample from raw medical datasets. Here, orthogonal local preserving projection (OLPP) is used to reduce the feature dimension. Once the feature reduction is formed, the prediction will be done based on the optimal classifier. In the optimal classifier, artificial bee colony algorithm will be used with neural network. The effectiveness of our proposed is measured in terms of accuracy, sensitivity and specificity. Here, Switzerland dataset achieves the maximum accuracy value 95.935%.
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