Optimal fuzzy min-max neural network for medical data classification using group search optimiser algorithm

Several techniques were applied to healthcare datasets for the prediction of future healthcare utilisation such as predicting individual expenditures and disease risks for patients. 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 to extract useful data and to convert suitable samples from raw medical datasets. 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. Once the feature reduction is formed, the prediction will be carried out based on the optimal classifier. In the optimal classifier, group search optimiser algorithm will be used for fuzzy min-max neural network. Here, the experimentation is done by using various datasets from UCI machine learning repository.