A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease

Vafa Radpour; Farhad Soleımanıan Gharehchopogh

doi:10.35377/saucis...978409

EN

A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease

Abstract

One of the essential aims of intelligent algorithms concerning the diagnosis of heart disease is to achieve accurate results and discover valuable patterns. This paper proposes a new hybrid model based on Binary Farmland Fertility Algorithm (BFFA) and Naïve Bayes (NB) to diagnose heart disease. The BFFA is used for Feature Selection (FS) and the NB for data classification. FS can be employed to discover the most beneficial features. Four valid and universal UCI datasets (Heart, Cleveland, Hungary and Switzerland) were used to diagnose heart disease. Each dataset included 13 main features. The evaluation of the proposed model is simulated in MATLAB 2017b. The number of features in four datasets of Heart, Cleveland, Hungary and Switzerland is equal to 13, which was reduced to six for each dataset through the BFFA to better the efficiency of the proposed model. For evaluation, the accuracy criterion, the criterion of accuracy in the proposed model for all features in the four datasets Heart, Cleveland, Hungary and Switzerland, is equal to 82.25%, 86.91%, and 89.32% 89.24%, respectively. Results of the proposed model showed appropriateness in comparison to some other methods. In this paper, the proposed model was compared with other methods, and it was found out that the proposed model possessed a better accuracy percentage.

Keywords

References

[1] M. Langarizadeh, S. M. A. Sadr-ameli, and M. Soleymani, “Development of Vital Signs Monitoring Decision Support System for Coronary Care Unit Inpatients,” Journal of Health Administration, vol. 20, no. 67, pp. 75-88, 2017.
[2] L. B. Sorkhabi, F. S. Gharehchopogh, and J. Shahamfar, “A systematic approach for pre-processing electronic health records for mining: case study of heart disease,” International Journal of Data Mining and Bioinformatics, vol. 24, no. 2, pp. 97-120, 2020.
[3] M. Hassanzadeh, I. Zabbah, and K. Layeghi, “Diagnosis of Coronary Heart Disease using Mixture of Experts Method,” Journal of Health and Biomedical Informatics, vol. 5, no. 2, pp. 274-285, 2015.
[4] S. M. S. Shah, F. A. Shah, S. A. Hussain, S. Batool, “Support Vector Machines-based Heart Disease Diagnosis using Feature Subset, Wrapping Selection and Extraction Methods”, Computers & Electrical Engineering, vol. 84, no. 1, pp. 106628, 2020.
[5] T. Vivekanandan, and N. C. S. N. Iyengar, “Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease,” Computers in Biology and Medicine, vol. 90, no. 1, pp. 125-136, 2017.
[6] S. M. S. Shaha, S. Batoolb, I. Khana, M. U. Ashrafac, S. H. Abbasa, S. A. Hussaina, “Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis,” Physica A: Statistical Mechanics and its Applications, vol. 482, no. 1, pp. 796-807, 2017.
[7] S. Nazari, M. Fallah, H. Kazemipoor, A. Salehipour, “A fuzzy inference- fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases,” Expert Systems with Applications, vol. 95, no.1, pp. 261-271, 2018.
[8] A.M. Alqudah, “Fuzzy expert system for coronary heart disease diagnosis in Jordan,” Health and Technology, vol. 7, no. 2, pp. 215-222, 2017.

[9] S. Javadzadeh, H. Shayanfar, and F. S. Gharehchopogh, “A Hybrid Model based on Ant Lion Optimization Algorithm and K-Nearest Neighbors Algorithm to Diagnose Liver Disease,” Ilam-University-of-Medical-Sciences, vol. 28, no. 5, pp. 76-89, 2020.
[10] M. H. F. Zarandi, A. Seifi, M. M. Ershadi, and H. Esmaeeli, “An Expert System Based on Fuzzy Bayesian Network for Heart Disease Diagnosis,” North American Fuzzy Information Processing Society Annual Conference, NAFIPS 2017: Fuzzy Logic in Intelligent System Design, vol. 648, pp. 191-201, 2017.
[11] S. Safdar, S. Zafar, N. Zafar, and N. F. Khan, “ learning based decision support systems (DSS) for heart disease diagnosis: a review,” Artificial Intelligence Review, vol. 50, no. 4, pp. 597-623, 2018.
[12] H. Shayanfar, and F. S. Gharehchopogh, “Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems,” Applied Soft Computing, vol. 71, pp. 728-746, 2018.
[13] Y. Jiang, H. Lin, X. Wang, and D. Lu, “A Technique for Improving the Performance of Naive Bayes Text Classification,” International Conference on Web Information Systems and Mining, WISM 2011: Web Information Systems and Mining, vol. 6988, pp. 196-203, 2011.
[14] A. Benyamin, F. S. Gharehchopogh, and S. Barshandeh, “Discrete farmland fertility optimization algorithm with metropolis acceptance criterion for traveling salesman problems,” International Journal of Intelligent Systems, vol. 36, no. 3, pp. 1270-1303, 2021
[15] A. Hosseinalipour, F. S. Gharehchopogh, M. Masdari, and A. Khademi, “A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology,” Applied Intelligence, vol. 51, pp. 4824-4859, 2021.
[16] S. Khalandi, and F. S. Gharehchopogh, “A New Approach for Text Documents Classification with Invasive Weed Optimization and Naive Bayes Classifier,” Journal of Advances in Computer Engineering and Technology, vol. 4, no. 3, pp. 167-184, 2018.
[17] S. Ardam, and F. S. Gharehchopogh, “Diagnosing Liver Disease using Firefly Algorithm based on Adaboost,” Journal of Health Administration, vol. 22, no. 1, pp. 61-77, 2019.
[18] V. Maihami, A. Khormehr, and E. Rahimi, “Designing an expert system for prediction of heart attack using fuzzy systems,” HBI_Journals, vol. 21, no. 4, pp. 118-131, 2016.
[19] M. Kazemi, H. Mehdizadeh, and A. Shiri, “Heart disease forecast using neural network data mining technique,” Ilam-University-of-Medical-Sciences, vol. 25, no. 1, pp. 20-32, 2017.
[20] Z. Hassani, and M. Khosravi, “Diagnosis of Coronary Heart Disease by Using Hybrid Intelligent Systems Based on the Whale Optimization Algorithm Simulated Annealing and Support Vector Machine,” Engineering Management and Soft Computing, vol. 4, no. 2, pp. 79-93, 2019.
[21] M. S. Mahmoodi, “Designing a Heart Disease prediction System using Support Vector Machine,” Journal of Health and Biomedical Informatics, vol. 4, no. 1, pp. 1-10, 2017.
[22] R. Akhoondi, and R. Hosseini, “A Novel Fuzzy-Genetic Differential Evolutionary Algorithm for Optimization of A Fuzzy Expert Systems Applied to Heart Disease Prediction,” Soft Computing Journal (SCJ), vol. 6, no. 2, pp. 32-47, 2017.
[23] H. Sabbagh Gol, “Detection of Coronary Artery Disease Using C4.5 Decision Tree,” Journal of Health and Biomedıcal Informatics, vol. 3, no. 4, pp. 287-299, 2017.
[24] Zabbah, M. Hassanzadeh, and Z. Koohjani, “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), vol. 4, no. 4, pp. 29-39, 2017.
[25] R. Safdari, M. Ghazi Saeedi, M. Gharooni, M. Nasiri, and G. Argi, “Comparing performance of decision tree and neural network in predicting myocardial infarction,” Journal of Paramedical Sciences & Rehabilitation, vol. 3, no. 2, pp. 26-35, 2014.
[26] Mahmoudi , R. A. Moghadam, M. H. Moazzam , S. Sadeghian, “Prediction model for coronary artery disease using neural networks and feature selection based on classification and regression tree,” Shahrekord-University-of-Medical-Sciences, vol. 15, no. 5, pp. 47-56, 2013.
[27] H. Tahmasbi, M. Jalali, and H. Shakeri, “An Expert System for Heart Disease Diagnosis Based on Evidence Combination in Data Mining,” Journal of Health and Biomedical Informatics, vol. 3, no. 4, pp. 251-258, 2017.
[28] S. Mirjalili, and A. Lewis, “S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization,” Swarm and Evolutionary Computation, vol. 9, pp. 1-14, 2013.
[29] Statlog, “statlog+(heart),” 1997.[Online]. Available: https://archive.ics.uci.edu/ml/datasets/statlog+(heart). [Accessed: 25-May-2021].
[30] cleveland ,” cleveland ,“ 2005, [Online]. Available: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/cleveland.data. [Accessed: 25-May-2021].
[31] hungarian ,,” hungarian ,“ 1998, [Online]. Available: https://archive.ics.uci.edu/ml/machine-learning-databases/heartdisease/processed.hungarian.data. [Accessed: 25-May-2021].
[32] switzerland ,” switzerland ,“ 2002, [Online]. Available:https://archive.ics.uci.edu/ml/machine-learning-databases/heartdisease/processed.switzerland.data. [Accessed: 25-May-2021].
[33] S. Nagpal, S. Arora, S. Dey, A. Shreya, “Feature Selection using Gravitational Search Algorithm for Biomedical Data. Procedia Computer Science,” vol. 115, no. 1, pp. 258-265, 2017.
[34] A. M. Alhassan, and W. M. W. Zainon, “Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis,” Applied Sciences, vol. 10, no. 18, pp. 1-20, 2020.

Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Vafa Radpour
0000-0001-5367-746X
Iran

Farhad Soleımanıan Gharehchopogh ^*
0000-0003-1588-1659
Iran

Publication Date

April 30, 2022

Submission Date

August 3, 2021

Acceptance Date

April 14, 2022

Published in Issue

Year 1970 Volume: 5 Number: 1

DOI

https://doi.org/10.35377/saucis...978409

IZ

https://izlik.org/JA57YP24FZ

Cite

RIS / Bibtex

APA

Radpour, V., & Soleımanıan Gharehchopogh, F. (2022). A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease. Sakarya University Journal of Computer and Information Sciences, 5(1), 90-103. https://doi.org/10.35377/saucis...978409

AMA

1.Radpour V, Soleımanıan Gharehchopogh F. A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease. SAUCIS. 2022;5(1):90-103. doi:10.35377/saucis.978409

Chicago

Radpour, Vafa, and Farhad Soleımanıan Gharehchopogh. 2022. “A Novel Hybrid Binary Farmland Fertility Algorithm With Naïve Bayes for Diagnosis of Heart Disease”. Sakarya University Journal of Computer and Information Sciences 5 (1): 90-103. https://doi.org/10.35377/saucis. 978409.

EndNote

Radpour V, Soleımanıan Gharehchopogh F (April 1, 2022) A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease. Sakarya University Journal of Computer and Information Sciences 5 1 90–103.

IEEE

[1]V. Radpour and F. Soleımanıan Gharehchopogh, “A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease”, SAUCIS, vol. 5, no. 1, pp. 90–103, Apr. 2022, doi: 10.35377/saucis...978409.

ISNAD

Radpour, Vafa - Soleımanıan Gharehchopogh, Farhad. “A Novel Hybrid Binary Farmland Fertility Algorithm With Naïve Bayes for Diagnosis of Heart Disease”. Sakarya University Journal of Computer and Information Sciences 5/1 (April 1, 2022): 90-103. https://doi.org/10.35377/saucis. 978409.

JAMA

1.Radpour V, Soleımanıan Gharehchopogh F. A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease. SAUCIS. 2022;5:90–103.

MLA

Radpour, Vafa, and Farhad Soleımanıan Gharehchopogh. “A Novel Hybrid Binary Farmland Fertility Algorithm With Naïve Bayes for Diagnosis of Heart Disease”. Sakarya University Journal of Computer and Information Sciences, vol. 5, no. 1, Apr. 2022, pp. 90-103, doi:10.35377/saucis. 978409.

Vancouver

1.Vafa Radpour, Farhad Soleımanıan Gharehchopogh. A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease. SAUCIS. 2022 Apr. 1;5(1):90-103. doi:10.35377/saucis. 978409

A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease

Öz

A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease

Abstract

Keywords

References

Details

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Cited By

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