Ant Lion Optimization Based Medical Data Classification Using Modified Neuro Fuzzy Classifier

The progression of converting depiction of medical analysis and measures into widespread medical code numbers is known as medical classification. According to the conclusion of medical investigation or description of medical handling, the classification of difficulty is resolved by means of the medical specialist in medical region. Primarily preprocessing is employed to alter appropriate model from unprocessed medical datasets in obtainable medical data classification process. After that, orthogonal local preserving projection (OLPP) is exploited to diminish the great dimensions of attribute. At last, the combination of artificial bee colony algorithm with neural network is used for classification of disease. This progression is extra time overriding and encompass poor exactness because classification is carry out devoid of removing the significant attribute from the medical dataset. Therefore, the proposed method is employed to eliminate of these difficulty. At this point the preprocessing is carrying out the medical dataset. It aids to eliminate the unnecessary noises and deficiency take place in the unprocessed medical datasets. Afterward feature choice is carry out. In medical data classification, feature choice is as well recognized as variable choice, attributes choice or variable subset choice which is the progression of choosing a subset of related attribute (variables, predictors). The ant lion optimization (ALO) is utilized for this intention. After that, classification is carrying out for distinguishing the occurrence and nonappearance of diseases. The intention is bring about by means of Modified Neuro Fuzzy classifier. The regulations engender through this classifier which are optimized by means of fruit fly optimization algorithm (FFOA). Therefore the medical data can be categorized into normal or abnormal one. This process task is quicker and generates extra precise consequences than the obtainable process. The presentation of the proposed process is calculated in expressions of accuracy, specificity and sensitivity value. The proposed process is executed in MATLAB.