Title: Computer assisted medical decision-making system using genetic algorithm and extreme learning machine for diagnosing allergic rhinitis
Authors: V.R. Elgin Christo; H. Khanna Nehemiah; Kindie Biredagn Nahato; J. Brighty; A. Kannan
Addresses: Ramanujan Computing Centre, Anna University, Chennai, Tamil Nadu, India ' Ramanujan Computing Centre, Anna University, Chennai, Tamil Nadu, India ' Information Systems Department, Debre Berhan Universty, Ethiopia ' Department of Computer Science and Engineering, Anna University, Chennai, Tamil Nadu, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Abstract: Allergic rhinitis (AR) is an antigen-mediated inflammation of the nasal mucosa that might extend into the paranasal sinuses. Rhinorrhea, nasal obstruction or blockage, nasal itching, sneezing, and postnasal drip that reverse spontaneously or after treatment are symptoms of AR. Allergic conjunctivitis frequently accompanies AR. For diagnosis of AR, intradermal skin tests remain the gold standard. This paper presents a clinical decision-making system that assists the clinicians to diagnose whether a patient suffers from AR. Feature selection is done using a wrapper approach that employs genetic algorithm (GA) and extreme learning machine (ELM). The selected features are trained and tested using an ELM classifier. For experimenting, the outcome of the symptoms observed in 872 patients for diagnosing the presence or absence of AR has been used. The experimental result shows that the system has achieved an accuracy of 97.7%.
Keywords: genetic algorithm; extreme learning machine; ELM; computer assisted decision making; allergy and immunology; machine learning.
International Journal of Bio-Inspired Computation, 2020 Vol.16 No.3, pp.148 - 157
Accepted: 01 Apr 2020
Published online: 03 Nov 2020 *