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:: Volume 28, Issue 5 (12-2020) ::
Journal of Ilam University of Medical Sciences 2020, 28(5): 76-89 Back to browse issues page
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 * 3
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:   (1622 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]   (851 Downloads)    
Type of Study: Research | Subject: biostatistics
Received: 2020/01/6 | Accepted: 2020/09/1 | Published: 2020/12/30
References
1. Calvopina DA, Noble C, Weis A, Hartel GF, Ramm GA. Supersonic shear wave elastography and APRI for the detection and staging of liver disease in pediatric cystic fibrosis. J Cyst Fibros2019; 2:1-6. doi.10.1016/j.jcf.2019.06.017
2. Lewindon PJ, Puertolas-Lopez MV, Ramm LE, Noble C, Ramm GA. Accuracy of transient elastography data combined with APRI in detection and staging of liver disease in pediatric patients with cystic fibrosis. Clin Gastroenterol Hep 2019; 17: 2561-9. doi.10.1016/j.cgh.2019.03.015.
3. Vanderlocht J, Cruys MVD, Stals F, Bakker L, Damoiseaux J. Multiplex autoantibody detection for autoimmune liver diseases and autoimmune gastritis. J Immunolo Meth 2017; 448: 21-5. doi.10.1016/j.jim.2017.05.003.
4. Gharehchopogh FS, Mousavi SK. [A decision support system for diagnosis of diabetes and hepatitis. based on the combination of particle swarm optimization and firefly algorithm]. J Health Bio Inform 2019; 6: 32-45. (Persian)
5. Gharehchopogh FS, Shayanfar H, Gholizadeh H. A comprehensive survey on symbiotic organisms search algorithms. Art Int Rev 2019; 1-48. doi.10.1007/s10462-019-09733-4
6. Gharehchopogh FS, Gholizadeh H. A comprehensive survey: whale optimization algorithm and its applications. Swarm Evol Comput2019; 48: 1-24. doi. 10.1016/j.swevo.2019.03.004
7. Gharehchopogh FS, FarokhZad MR. [Determining fuzzy logic parameters by using genetic algorithm for the diagnosis of liver disease]. J Health a Bio Info2018; 5: 384-39. (Persian)
8. Abdar M, Zomorodimoghadam M, Das R, Ting IH. Performance analysis of classification algorithms on early detection of liver disease. Exp Syst Appl2017; 67: 239-51. doi.10.1016/j.eswa.2016.08.065
9. Joloudari JH, Saadatfar H, Dehzangi A, Shamshirband S. Computer aided decision-making for predicting liver disease using PSO based optimized SVM with feature selection. Info Med Unlocked 2019; 17: 1-17. doi.org/10.1016/j.imu.2019.100255
10. Pourpanah F, Tan CJ, Lim CP, Mohamad J. A Q-learning based multi agent system for data classification. Appl Soft Comput 2017; 52: 519-31. doi. 10.1016/j.asoc.2016.10.016
11. Weng CH, Cheng T, Han RP. Disease prediction with different types of neural network classifiers. Tel Info 2016; 33: 277-92. doi.10.1016/j.tele.2015.08.006
12. Liang C, Peng L. An automated diagnosis system of liver disease using artificial immune and genetic algorithms. J Med Syst 2013; 2:1-10. doi.10.1007/s10916-013-9932-9
13. Kumar P, Thakur RS. Diagnosis of liver disorder using fuzzy adaptive and neighbor weighted k-nn method for lft imbalanced data. Int Con Struc Syst 2019; 3:1-5. doi.10.1109/ICSSS.2019.8882861
14. Rajeswari P, Reena GS. Analysis of liver disorder using data mining algorithm. Global J Comput Sci Technol2010; 2:71-6.
15. Mirjalili S. The ant lion optimizer. Adv Eng Soft2015; 83: 80-98. doi. 10.1016/j.advengsoft.2015.01.010
16. Martin B. Instance Based Learning: Nearest Neighbour with Generalisation. Uni Waikato Dept Comput Sci Newzealand1995; 95:1-76.
17. Mahmoudi M, Gharehchopogh FS. an improvement of shuffled frog leaping algorithm with a decision tree for feature selection in text document classification. CSI J Compu Sci Eng 2018; 16: 60-72.
18. Orooji A, Langarizadeh M. Evaluation of the effect of feature selection and different kernel functions on svm performance for breast cancer diagnosis. J Health Biomed Info2018; 5:244-51. doi.jhbmi.ir/article-1-284-en.html
19. Allahverdipour A, Gharehchopogh FS. An improved K-nearest neighbor with crow search algorithm for feature selection in text documents classification. J Adv Compu Res 2018; 9: 37-48. doi.jacr.iausari.ac.ir/article_655529.html
20. Jain S, Shukla S, Wadhvani R. Dynamic selection of normalization techniques using data complexity measures. Exp Syst Appl 2018; 106: 252-62. doi. 10.1016/j.eswa.2018.04.008
21. Han J, Kamber M. Data mining concepts and techniques. 2 th ed. Morgan Kuafmann Publication. 2006; P.133-9. doi.10.1016/C2009-0-61819-5
22. Wu H, Yang S, Huang Z, He J, Wang X. Type 2 diabetes mellitus prediction model based on data mining. Info Med Unlocked 2018; 10:100-07. doi.10.1016/j.imu.2017.12.006
23. Edla DR, Cheruku R. Diabete finder: a bat optimized classification system for type 2 diabetes. Proce Comput Sci 2017; 115: 235-42. doi.10.1016/j.procs.2017.09.130
24. Abdar M, Yen NY, Hung JCS. Improving the diagnosis of liver disease using multilayer perceptron neural network and boosted decision trees. J Med Bio Eng2018; 38: 953-65. doi.10.1007/s40846-017-0360-z
25. Bashir S, Qamar U, Khan FH. Intelli health a medical decision support application using a novel weighted multi-layer classifier ensemble framework. J Biomed Info 2015; 59: 185-200. doi.10.1016/j.jbi.2015.12.001
26. Zhou J, Lai Z, Gao C, Miao D, Yue X. Rough possibilistic C-means clustering based on multigranulation approximation regions and shadowed sets. Knowl Base Syst 2018; 160: 144-66. doi.10.1016/j.knosys.2018.07.007
27. Singh J, Bagga S, Kaur R. Software based prediction of liver disease with feature selection and classification techniques. Proce Comput Sci2020; 167: 1970-80. doi. 10.1016/j.procs.2020.03.226
28. Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Soft 2014; 69: 46-61. doi.10.1016/j.advengsoft.2013.12.007
29. Kennedy J, Eberhart RC. Particle swarm optimization. Proce Int Con Neur Net 1995;3: 1942-8. doi.10.1109/ICNN.1995.488968
30. Karaboga D. An idea based on honeybee swarm for numerical optimization technical report tr06 Erciyes University engineering faculty. Com Eng Dep2005; 4:81-6. doi. 015d/f4d97ed1f541752842c49d12e429a785460b.pdf
31. Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Soft 2016; 95: 51-67. doi.10.1016/j.advengsoft.2016.01.008
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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. Journal title 2020; 28 (5) :76-89
URL: http://sjimu.medilam.ac.ir/article-1-6271-en.html


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Volume 28, Issue 5 (12-2020) Back to browse issues page
مجله دانشگاه علوم پزشکی ایلام Journal of Ilam University of Medical Sciences
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