Abstract
The main objective of this research is to develop a framework to classify the medical data. In order to achieve promising results in medical data classification, we have planned to utilize orthogonal local preserving projection and classifier. Initially, the pre-processing will be applied to extract useful data and to convert suitable sample from raw medical datasets. Here, input dataset will be as high dimensional or high features; so the high number of feature is a great obstruction for prediction. Therefore, feature dimension reduction method will be applied to reduce the features space without losing the accuracy of prediction. Here, orthogonal local preserving projection (OLPP) will be used to reduce the feature dimension. Once the feature reduction is formed, the prediction will be done based on the classifier.
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