Medical Data Classification using Fuzzy Min Max Neural Network Preceded by Feature Selection through Moth Flame Optimization

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.

@article{Dehariya2020,
  title     = {Medical Data Classification using Fuzzy Min Max Neural Network Preceded by Feature Selection through Moth Flame Optimization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {12},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Ashish Kumar Dehariya and Pragya Shukla},
  doi       = {10.14569/IJACSA.2020.0111276},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111276}
}