Title: Improved artificial neural network with aid of artificial bee colony for medical data classification
Authors: Balasaheb Tarle; Sudarson Jena
Addresses: Gitam University Vishakhapatnam, Hyderabad Campus, TL, India ' Gitam University Vishakhapatnam, Hyderabad Campus, TL, India
Abstract: The ultimate aim of the proposed method is to establish a model for classification of medical data. Various methods have been generated to health related data to detect upcoming health fitness usage including detecting person's spending and illness related issues for diseased persons. In order to achieve promising results in medical data classification, we have planned to utilise orthogonal local preserving projection and optimal classifier. Initially, the pre-processing will be applied for extracting useful information and to convert suitable sample from raw medical datasets. Here, orthogonal local preserving projection (OLPP) is used to reduce the feature dimension. Once the feature reduction is formed, the prediction will be done based on the optimal classifier. In the optimal classifier, artificial bee colony algorithm will be used with neural network. The effectiveness of our proposed is measured in terms of accuracy, sensitivity and specificity. Here, Switzerland dataset achieves the maximum accuracy value 95.935%.
Keywords: orthogonal local preserving projection; OLPP; classifier; neural network; artificial bee colony algorithm.
International Journal of Business Intelligence and Data Mining, 2019 Vol.15 No.3, pp.288 - 305
Received: 11 Apr 2017
Accepted: 22 Jun 2017
Published online: 04 Jul 2019 *