A Hybrid Model based on Ant Lion Optimization Algorithm 
and K-Nearest Neighbors Algorithm to
 Diagnose Liver Disease

Javadzadeh, Shayan; Shayanfar, Human; Soleimanian Gharehchopogh, Farhad

doi:10.29252/sjimu.28.5.76

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Volume 28, Issue 5 (11-2020)

Journal of Ilam University of Medical Sciences 2020, 28(5): 76-89

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A Hybrid Model based on Ant Lion Optimization Algorithm and K-Nearest Neighbors Algorithm to Diagnose Liver Disease

Shayan Javadzadeh¹

, Human Shayanfar²

, Farhad Soleimanian Gharehchopogh ^*

1- Dept of Computer Engineering, Kamal Institute of Higher Education, Urmia, Iran
2- Dept of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
3- Dept of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran , bonab.farhad@gmail.com

Abstract: (2067 Views)

Introduction: Given that a huge amount of cost is imposed on public and private hospitals from the department of liver diseases, it is necessary to provide a method to predict liver diseases. This study aimed to propose a hybrid model based on the Ant Lion Optimization algorithm and K-Nearest Neighbors algorithm to diagnose liver diseases.

Materials & Methods: This descriptive-analytic study proposed a hybrid model based on machine learning algorithms to classify individuals into two categories, including healthy and unhealthy (those with liver diseases). The proposed model has been simulated using MATLAB software. The datasets used in this study were obtained from the Indian Liver Patient Dataset available in the Machine Learning Repository at the University of Irvine, California. This dataset contains 583 independent records, including 10 features for liver diseases.

Findings: After pre-processing, the dataset was randomly divided into 20 categories of the entire dataset, which included different training and test data. In each category of the dataset, 90% and 10% of the data were used for training and test, respectively. Regarding all features, the results obtained the most accurate mode at 95.23%. Moreover, according to the criteria of specificity and sensitivity accuracy, the corresponding values were 93.95% and 94.11%, respectively. Furthermore, the accuracy of the proposed model along with five features was estimated at 98.63%.

Discussions & Conclusions: This model was proposed to diagnose and classify liver diseases along with an accuracy rate of higher than 90%. Healthcare centers and physicians can utilize the results of this study.

Keywords: Ant lion optimization (ALO) algorithm, Classification, Diagnosis of liver disease, K-nearest neighbors (KNN) algorithm

Full-Text [PDF 808 kb] (1074 Downloads)

Type of Study: Research | Subject: biostatistics
Received: 2020/01/6 | Accepted: 2020/09/1 | Published: 2020/12/30

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Javadzadeh S, Shayanfar H, Soleimanian Gharehchopogh F. A Hybrid Model based on Ant Lion Optimization Algorithm and K-Nearest Neighbors Algorithm to Diagnose Liver Disease. J. Ilam Uni. Med. Sci. 2020; 28 (5) :76-89
URL: http://sjimu.medilam.ac.ir/article-1-6271-en.html

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