Abstract
Prediction of the diseases are possible using medical diagnosis system. This type of health care model can be developed using soft computing techniques. Hybrid approaches of data classification and optimization algorithm increases data classification accuracy. This research proposed applications of Moth Flame optimization (MFO) and Fuzzy Min Max Neural Network (FMMNN) for the development of medical data classification system. Here MFO algorithm considers bulk of features from the disease dataset and produces optimized set of features based on fitness function. MFO is able to avoid local minima problem and this is the main cause behind production of optimal set of features. Optimized features are then passed to FMMNN for classification of malignant and benign cases. As classification is concerned, model experiment achieved 97.74% accuracy for Liver Disorders and 86.95 % accuracy for Pima Indian Diabetes dataset. Improving the medical data classification accuracy is directly related to attain good human health.
Highlights
Nowadays, specialized computer software is very popular for medical data classification
Medical data classification tool needed and demanded in medical field because it overcomes the problem like difficulties in huge medical data analysis by medical professionals means they hardly processes the large amount of medical data, sometimes they may suffer with fatigue or other tensions resulting classification error
Moth Flame Optimization (MFO) generates optimal set of features to be supplied to classifier Fuzzy Min Max Neural Network (FMMNN) for achieving classification accuracy with less error
Summary
Nowadays, specialized computer software is very popular for medical data classification. For the development of smart healthcare system in smart cities use medical diagnosis software plays a vital role In this type of medical data classification tool/software, patient’s disease symptoms are generally learned this learned knowledge is used for deciding the facts whether patients are suffering from disease or not. Medical data classification tool needed and demanded in medical field because it overcomes the problem like difficulties in huge medical data analysis by medical professionals means they hardly processes the large amount of medical data, sometimes they may suffer with fatigue or other tensions resulting classification error They need a tool/ expert software that can help them to make a justified decision. In our model we are using Moth Flame Optimization (MFO) for feature selection and Fuzzy Min Max Neural Network (FMMNN) for Classification of disease datasets. MFO generates optimal set of features to be supplied to classifier FMMNN for achieving classification accuracy with less error
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