Self Organising Fuzzy Logic Classifier for Predicting Type-2 Diabetes Mellitus using ACO-ANN

In today’s digital world, a dataset with large number of attributes has a curse of dimensionality where the computation time grows exponentially with the number of dimensions. To overcome the problem of computation time and space, appropriate method of feature selection can be developed using metaheuristic approaches. The aim of this work is to investigate the use of ant colony optimization with the help of neural network to select near optimal feature subset and integrate it with the self-organizing fuzzy logic classifier for improving the recognition rate. The proposed fuzzy classifier derives prototype from the collected data through an offline training process and uses it to develop a fuzzy inference system for classification. Once trained, it can continuously learn from streaming data and later adapts the changing facts by updating the system structure recursively. The developed model is not based on predefined parameters used in the data generation model but is derived from the empirically observed data.

@article{Patil2020,
  title     = {Self Organising Fuzzy Logic Classifier for Predicting Type-2 Diabetes Mellitus using ACO-ANN},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {7},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Ratna Patil and Sharvari Tamane and Kanishk Patil},
  doi       = {10.14569/IJACSA.2020.0110746},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110746}
}