Enhanced Neuro-Fuzzy System Based on Genetic Algorithm for Medical Diagnosis
|Asogbon MG1, Samuel OW2,3,*, Omisore MO3 and Awonusi O4|
|1Department of Computer Science, Federal University of Technology Akure, Ondo State, P.M.B. 704, Nigeria|
|2Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, CAS, Shenzhen, Guangdong 518055, China|
|3University of Chinese Academy of Sciences, Beijing, 100049, China|
|4Department of Information and Communication Technology, Prototype Engineering Development Institute, Ilesa, P.M.B. 5025, Nigeria|
|*Corresponding Author :||Samuel OW, PhD
1068 Xueyuan Avenue
University Town of Shenzhen
Xili, Nanshan, Shenzhen
Tel: +86 15814491870
E-mail: [email protected], [email protected]
|Received January 14, 2016; Accepted February 09, 2016; Published February 16, 2016|
|Citation: Asogbon MG, Samuel OW, Omisore MO, Awonusi O (2016) Enhanced Neuro-Fuzzy System Based on Genetic Algorithm for Medical Diagnosis . J Med Diagn Meth 5:205. doi:10.4172/2168-9784.1000205|
|Copyright: © 2016 Asogbon MG, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.|
Objective: The aim of this study was to optimize the performance of an Adaptive Neuro-Fuzzy Inference System (ANFIS) in terms of its connection weights which is usually computed based on trial and error when used to diagnose Typhoid fever patients.
Methods: This research proposed the use of Genetic Algorithm (GA) technique to automatically evolve optimum connection weights needed to efficiently train a built ANFIS model used for Typhoid fever diagnosis. The GA module computes the best set of connection weights, stores them, and later supplies them to the corresponding hidden layer nodes for training the ANFIS. The medical record of 104 Typhoid fever patients aged 15 to 75 were used to evaluate the performance of the multi-technique decision support system. 70% of the dataset was used training data, 15% was used for validation while the remaining 15% was used to observe the performance of the proposed system.
Results: From the evaluation results, the proposed Genetic Adaptive Neuro Fuzzy Inference System (GANFIS) achieved an average diagnosis accuracy of 92.7% compared to 85.4% recorded by the ANFIS method. It was equally observed that the diagnosis time was much lower for the proposed method when compared to that of ANFIS.
Conclusion: Therefore, the proposed system (GANFIS) has the capability to attenuate the key problems associated with Neuro-Fuzzy Based diagnostic methods if fully embraced and as well it could be adopted to solve challenging problems in several other domains.