Volume 23, Issue 3 (10-2020)
jha 2020, 23(3): 42-54 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
DezhAloud N, Soleimanian Gharehchopogh F. Diagnosis of Heart Disease Using Binary Grasshopper Optimization Algorithm and K-Nearest Neighbors. jha 2020; 23 (3) :42-54
URL: http://jha.iums.ac.ir/article-1-3297-en.html
URL: http://jha.iums.ac.ir/article-1-3297-en.html
1- Msc Student, Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.
2- Assistant Professor, Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia,Iran. ,bonab.farhad@gmail.com
2- Assistant Professor, Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia,Iran. ,
Abstract: (3468 Views)
Introduction: The heart is one of the main organs of the human body, and its unhealthiness is an important factor in human mortality. Heart disease may be asymptomatic, but medical tests can predict and diagnose it. Diagnosis of heart disease requires extensive experience of specialist physicians. The aim of this study is to help physicians diagnose heart disease based on hybrid Binary Grasshopper Optimization (BGO) Algorithm and K-Nearest Neighbors (KNN). The BGO algorithm is used for feature selection (FS), and the KNN is used for classification.
Methods: In this study, the medical records of 270 patients in the field of heart disease with 13 features were evaluated. The number of patients is equal to 120 and the absence of disease is equal to 150, so the data set is balanced. Patient information is taken from the standard UCI (University of California, Irvine) database. The evaluation of the proposed model has been done in MATLAB simulation.
Results: According to the evaluations, the accuracy was 89.82%, the sensitivity was 89.61%, and the specificity was 90.41%, which are acceptable compared to the results of previous studies in the field of heart disease. Also, the percentage of accuracy of the proposed method based on 7 features (Age, Sex, Chest Pain, BP, Electrocardiographic, Angina, and Thallium) is equal to 90.35%.
Conclusion: According to the results of this study, for the diagnosis of heart disease, the proposed method has been more effective in diagnosing the disease and selecting important features in comparison with previous methods.
Methods: In this study, the medical records of 270 patients in the field of heart disease with 13 features were evaluated. The number of patients is equal to 120 and the absence of disease is equal to 150, so the data set is balanced. Patient information is taken from the standard UCI (University of California, Irvine) database. The evaluation of the proposed model has been done in MATLAB simulation.
Results: According to the evaluations, the accuracy was 89.82%, the sensitivity was 89.61%, and the specificity was 90.41%, which are acceptable compared to the results of previous studies in the field of heart disease. Also, the percentage of accuracy of the proposed method based on 7 features (Age, Sex, Chest Pain, BP, Electrocardiographic, Angina, and Thallium) is equal to 90.35%.
Conclusion: According to the results of this study, for the diagnosis of heart disease, the proposed method has been more effective in diagnosing the disease and selecting important features in comparison with previous methods.
Keywords: Heart Disease Detection, Binary grasshopper optimization Algorithm, K-Nearest Neighbor, Classification
Type of Study: Research |
Subject:
Health Information Management
Received: 2020/07/7 | Accepted: 2020/10/1 | Published: 2020/10/1
Received: 2020/07/7 | Accepted: 2020/10/1 | Published: 2020/10/1
References
1. Shekar KC, Chandra P, Rao KV. A framework for automatic detection of heart diseases using dynamic deep neural activation functions. Journal of Ambient Intelligence and Humanized Computing. 2020; 1-15.
2. Kausar N, Palaniappan S, Samir BB, Abdullah A, Dey N. (2016) Systematic Analysis of Applied Data Mining Based Optimization Algorithms in Clinical Attribute Extraction and Classification for Diagnosis of Cardiac Patients. In: Hassanien AE, Grosan C, Fahmy Tolba M. (eds) Applications of Intelligent Optimization in Biology and Medicine. Intelligent Systems Reference Library. Springer. Cham; 2015. p. 217-231. [DOI:10.1007/978-3-319-21212-8_9]
3. Publication bias in clinical research [Internet]. 2018 [2018 May 24; cited 2020 Sep 05]; Available from: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
4. ayaraman V, Sultana HP. Artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network for feature selection in heart disease classification. Journal of Ambient Intelligence and Humanized Computing. 2019; 1-15. [DOI:10.1007/s12652-019-01193-6]
5. Amin MS, Chiam YK, Varathan KD. Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics. 2019; 36: 82-93. [DOI:10.1016/j.tele.2018.11.007]
6. Parks R, Wigand RT, Othmani MB, Serhier Z, Bouhaddou O. Electronic health records implementation in Morocco: Challenges of silo efforts and recommendations for improvements. International Journal of Medical Informatics. 2019; 129: 430-437. [DOI:10.1016/j.ijmedinf.2019.05.026]
7. Rudolf JW, Dighe AS. Decision Support Tools within the Electronic Health Record. Clinics in Laboratory Medicine. 2019; 39(2): 197-213. [DOI:10.1016/j.cll.2019.01.001]
8. Liu L, Zhao S, Chen H, Wang A. A new machine learning method for identifying Alzheimer's disease. Simulation Modelling Practice and Theory. 2020; 99:1-20. [DOI:10.1016/j.simpat.2019.102023]
9. Ullah R, Khan S, Ali H, Chaudhary II, Ahmad I. A comparative study of machine learning classifiers for risk prediction of asthma disease. Photodiagnosis and Photodynamic Therapy. 2019; 28: 292-296. [DOI:10.1016/j.pdpdt.2019.10.011]
10. Shayanfar H, Gharehchopogh FS. Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems. Applied Soft Computing. 2018; 71: 728-746. [DOI:10.1016/j.asoc.2018.07.033]
11. 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. Journal of Health and Biomedical Informatics. 2019; 6 (1) :32-45. [In Persian].
12. Ardam S, Soleimanian Gharehchopogh F. Diagnosing Liver Disease using Firefly Algorithm based on Adaboost. Journal of Health Administration. 2019; 22 (1) :61-77. [In Persian].
13. Kazemi M, Mehdizadeh H, Shiri A. Heart disease forecast using neural network data mining technique. sjimu. 2017; 25 (1) :20-32. [In Persian]. [DOI:10.29252/sjimu.25.1.20]
14. Heravi M, Setayeshi S. Intelligent and fast recognition of heart disease based on synergy of linear neural network and logistic regression model. J Mazandaran Univ Med Sci. 2014; 24 (112) :78-87. [In Persian]
15. Sabbagh_Gol H. Detection of Coronary Artery Disease Using C4.5 Decision Tree. Journal of Health and Biomedical Informatics. 2017; 3 (4) :287-299. [In Persian]
16. Mahmoodi MS. Designing a Heart Disease prediction System using Support Vector Machine. Journal of Health and Biomedical Informatics. 2017; 4 (1) :1-10. [In Persian]
17. Nagpal S, Arora S, Dey S, Shreya. Feature Selection using Gravitational Search Algorithm for Biomedical Data. Procedia Computer Science. 2017; 115: 258-265. [DOI:10.1016/j.procs.2017.09.133]
18. Zabbah I, Hassaanzadeh M, kohjani Z. The Effect of Continuous Parameters on the Diagnosis of Coronary Artery Disease Using Artificial Neural Networks. Journal of Torbat Heydariyeh University of Medical Sciences (Journal of Health Chimes). 2017; 4 (4) :29-39. [In Persian]
19. Mafarja M, Aljarah I, Faris H, Hammouri AI, Al-Zoubi AM, Mirjalili S. Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Systems with Applications. 20149; 117: 267-286. [DOI:10.1016/j.eswa.2018.09.015]
20. Martin B. Instance-Based Learning: Nearest Neighbour with Generalisation [PhD thesis]. Hamilton: New Zealand; University of Waikato; 1995.
21. Publication bias in clinical research [Internet]. 2004 [Updated 2004; cited 2020 Sep 05]; Available from: http://archive.ics.uci.edu/ml/datasets/statlog+(heart)
22. Wang Y, Li T. Local feature selection based on artificial immune system for classification. Applied Soft Computing. 2020; 87: 1-27. [DOI:10.1016/j.asoc.2019.105989]
23. Tubishat M, Idris N, Shuib L, Abushariah MAM, Mirjalili S. Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection. Expert Systems with Applications. 2020; 145:1-23. [DOI:10.1016/j.eswa.2019.113122]
24. Ghaemi M, Feizi-Derakhshi MR. Feature selection using Forest Optimization Algorithm. Pattern Recognition. 2016; 60: 121-129. [DOI:10.1016/j.patcog.2016.05.012]
25. Pashaei E, Aydin N. Binary black hole algorithm for feature selection and classification on biological data. Applied Soft Computing. 2017; 56: 94-106. [DOI:10.1016/j.asoc.2017.03.002]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Site Keywords
Health, Administration, Scientific Article, Iran University of Medical Sciences,
