Research Article
Hybrid Algorithms of Whale optimization algorithm and k-nearest neighbor to Predict the liver disease
@ARTICLE{10.4108/eai.13-7-2018.156838, author={Vahid Hajihashemi and Zeinab Hassani and Iman Sahraei Dehmajnoonie and Keivan Borna}, title={Hybrid Algorithms of Whale optimization algorithm and k-nearest neighbor to Predict the liver disease}, journal={EAI Endorsed Transactions on Context-aware Systems and Applications}, volume={6}, number={16}, publisher={EAI}, journal_a={CASA}, year={2019}, month={3}, keywords={Whale Optimization Algorithm, K-Nearest Neighbor Algorithm, Liver Disease, Medical data, Evolutionary algorithm}, doi={10.4108/eai.13-7-2018.156838} }- Vahid Hajihashemi
Zeinab Hassani
Iman Sahraei Dehmajnoonie
Keivan Borna
Year: 2019
Hybrid Algorithms of Whale optimization algorithm and k-nearest neighbor to Predict the liver disease
CASA
EAI
DOI: 10.4108/eai.13-7-2018.156838
Abstract
Liver Disease is one of the most common diseases which can be prevented by early diagnosis and up-todate treatment. Advances in machine learning and intelligence techniques have led to the effective diagnosis and prediction of diseases to improve the treatment of patients and reduce the cost of treatment. Whale Optimization Algorithm is a swarm intelligent technique, inspired by the social behavior of whales. One of the effective classification algorithms is K-Nearest Neighbor which is employed for pattern recognition. This paper was designed to investigate the prediction of Liver Disease using a hybrid algorithm including KNN and WOA. In order to evaluate the efficiency of hybrid algorithm, two datasets of liver disease including BUPA and ILPD were used. The results showed that 81.24% and 91.28% of accuracy was gained by the proposed algorithm for BUPA and ILPD, respectively. Experimental results showed that the hybrid WON-KNN is a better classifier to predict the liver diseases.
Copyright © 2019 Vahid Hajihashemi et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.