Heart Diseases Prediction for Optimization based Feature Selection and Classification using Machine Learning Methods

Globally, heart disease is considered to be the major cause of death. As per statistics, 17.9 million people are losing their lives every year worldwide. Chronic Kidney Disease (CKD) and Breast Cancer takes the next positions in the list. Disease classification is an important issue that needs more attention now. Making use of an optimized technique for such classification would be a better option. In this heart disease classification, initially, feature selection was done using Teaching learning based Optimization based (TLO) and Kernel Density. TLO is based on the process of classroom teaching, which involves too much iteration that leads to time complexity. Similarly, a certain level of misclassifications has been observed by using Kernel Density (KD). In the proposed method, K-Nearest Neighbour (KNN) is used to address the issue of NaN values and Density based Modified Teaching Learning based Optimization (DMTLO) is used for feature selection. Finally the classification process is done by considering Support Vector Machine (SVM) and Ensemble (Adaboosting method). SVM categorizes data bydissimilar class names by defining a group of support vectors that are part of the group of training inputs that plan a hyper plane in the attribute space. Ensemble method is used to solve statistical, computational and representational problems. Experimental outcomes have proved that the projected DMTLOovertakes the existing methodologies with required quantity of attributes.

@article{Rajinikanth2021,
  title     = {Heart Diseases Prediction for Optimization based Feature Selection and Classification using Machine Learning Methods},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {2},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {N. Rajinikanth and L. Pavithra},
  doi       = {10.14569/IJACSA.2021.0120280},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120280}
}