COMBINATION OF LOGISTIC REGRESSION AND SVM ALGORITHM WITH HYBRID PSO AND GA BASED SELECTION FEATURE IN CORONARY HEART DISEASE CLASSIFICATION
Keywords:
SVM Logistic Regression Classification, Selection Feature, PSO-GA
Abstract
The world's high death rate from heart disease requires early prevention by medical doctors to diagnose heart disease early. The machine learning approach makes it possible to predict the risk of developing heart disease by examining certain values at a low cost. This study will contribute to the development of a combination of Logistic Regression and SVM models that integrate SVM and Logistic Regression algorithms by implementing selection features using hybrid PSO and GA methods. The combination concept of Logistic Regression SVM (LRSVM) applied to this study is to reduce the risk of SVM output errors by interpreting and modifying the output of SVM classifiers by the results of Logistic Regression analysis. The test results showed that LRSVM with pso-GA hybrid-based selection feature achieved better performance for coronary heart disease classification with 99.66% accuracy compared to classification accuracy with SVM algorithm without selection feature
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References
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[3] Fariza, A., Martiana, E., Sucipto H. 2016. Aplikasi Algoritma Genetika Multi Obyektif pada Traveling Salesman Problem. Politeknik Elektronika Negeri Surabaya ITS.
[4] Gen, M. dan Cheng, R. 1997. Genetic Algorithms and Engineering Design.Newyork: Jhon Wiley & Sons, Inc.
[5] Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Canada: Addison-Wesley Publishing.
[6] Indira K & Kanmani, 2016. Performance Analysis of Genetic Algorithm for Mining Association Rules. International Journal of Computer Science Issues, Vol. 9, Issue 2, No 1, March 2012. Department of CSE, Pondicherry Engineering College Puducherry, 605014, India.
[7] Karegowda, A. G., Manjunath, A.S. & Jayaram, M.A., 2011. Application of genetic algorithm Optimized neural network Connection weights for medical Diagnosis of Pima Indians diabetes, International Journal on Soft Computing (IJSC), Vol.2, No.2, May 2011. Dept. of Master of Computer Applications, Siddaganga Institute of Technology, Tumkur, India.
[8] Kartheeswaran, S. & Durairaj, D. D. C. 2015. A Hybrid Genetic Algorithm and Back-Propagation Artificial Neural Network Based Simulation System for Medical Image Reconstruction in Noise-Added Magnetic Resonance Imaging Data. 2015 Online International Conference on Green Engineering and Technologies (IC-GET 2015).
[9] Liu, J. & Kong, X. 2018. Artificial Intelligence in the 21st Century. International Seminar Special Section On Human-Centered Smart Systems And Technologies IEEE 2018.
[10] Louridas, P. & Ebert, C. 2016. Machine Learning. Published By The IEEE Computer Society.
[11] Obitko, Marek. (1998). Introduction To Genetic Algorithms. University of Applied Sciences.
[12] Ray, S. 2019. A Quick Review of Machine Learning Algorithms. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (Com-IT-Con), India, 14th -16th Feb 2019.
[13] Sivanandam, S. N. & Deepa S. N. 2015. Introduction to Genetic Algorithms. New York: Springer-Verlag Berlin Heidelberg.
[14] Sofge, D. A, 2002. Using Genetic Algorithm Based Variable Selection to Improve Neural Network Models for Real-World Systems. Proceedings of the 2002 International Conference on Machine Learning 7 Applications. Navy Center for Applied Research in Artificial Intelligence Naval Research Laboratory Washington, D.C., U.S.A.
[15] Savio, I & Chakraborty, U. K. 2019. Genetic Algorithm: An Approach on Optimization. Proceedings of the Fourth International Conference on Communication and Electronics Systems (ICCES 2019). https://towardsdatascience.com/accuracy-recall-precision-f-score-specificity-which-to-optimize-on-867d3f11124 dilihat tanggal 25 Oktober 2020
IEEE Conference Record # 45898; IEEE Xplore ISBN: 978-1-7281-1261-9
[16] Bashir, S., Khan, Z. S., Khan, F. H., Anjum, A. & Bashir, K. 2019. Improving Heart Disease Prediction Using Feature Selection Approaches. Proceedings of 2019 16th International Bhurban Conference on Applied Sciences & Technology (IBCAST) Islamabad, Pakistan, 8th – 12th January 2019.
[17] Luxmi Verma1, L., Srivastava, S. & Negi, P. C., (2016). A Hybrid Data Mining Model to Predict Coronary Artery Disease Cases Using Non-Invasive Clinical Data. Journal Medical System, Vol.40, Issue: 178, pp: 1-7.
[18] Zhu, C., Idemudia, C. U., & Feng, W. (2019). Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques. Informatics in Medicine Unlocked, 17(April)
[19] Abualigah, L. M., Khader, A. T., & Hanandeh, E. S. (2018). A New Feature Selection Method To Improve The Document Clustering Using Particle Swarm Optimization Algorithm. Journal of Computational Science, Vol. 25, pp. 1-40.Fong, S., Wong, R., & Vasilakos, A. V. (2015). Accelerated PSO swarm search feature selection for data stream mining big data. IEEE Transactions on Services Computing, Vol. 9(1).
[20] Silva, P. H., Luz, E., Zanlorensi, L. A., & Menotti, D., (2018). Multimodal Feature Level Fusion Based On Particle Swarm Optimization With Deep Transfer Learning. IEEE Congress on Evolutionary Computation.
[21] Xiao, Y., Wang, Y., & Sun, Y. (2018). Reactive Power Optimal Control Of A Wind Farm For Minimizing Collector System Losses. Energies.Tuegeh, M., Soeprijanto and Purnomo, M. H. (2009) ‘Modified improved particle swarm optimization for optimal’, Seminar Nasional Aplikasi Teknologi Informassi 2009 (SNATI 2009), 2009(Snati), pp. 85–90.
[22] Virgeniya, S. C., & Ramaraj, E. (2019). Predictive analytics using rule-based classification and hybrid logistic regression(HLR) algorithm for decision making. International Journal of Scientific and Technology Research, 8(10), 1509–1513.
[23] Kolukisa, B., Hacilar, H., Goy, G., Kus, M., Bakir-Gungor, B., Aral, A., Gungor, V. C., İşlem, K. B., Sistemleri, T., & Ankara, A. Ş. (2019). Diagnosis of Coronary Heart Disease via Classification Algorithms and a New Feature Selection Methodology. International Journal of Data Mining Science, 1(1), 8–15.
[24] Tuegeh, M., Soeprijanto, & Purnomo, M. H. (2009). Modified improved particle swarm optimization for optimal. Seminar Nasional Aplikasi Teknologi Informassi 2009 (SNATI 2009), 2009(Snati), 85–90.
Published
2021-06-12
How to Cite
Situmorang, S., Sirait, P., & Andri. (2021). COMBINATION OF LOGISTIC REGRESSION AND SVM ALGORITHM WITH HYBRID PSO AND GA BASED SELECTION FEATURE IN CORONARY HEART DISEASE CLASSIFICATION. INFOKUM, 9(2, June), 204-210. Retrieved from http://seaninstitute.org/infor/index.php/infokum/article/view/113
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