A Review of Feature Selection and Classification Approaches for Heart Disease Prediction

https://doi.org/10.22146/ijitee.59193

Fathania Firwan Firdaus(1*), Hanung Adi Nugroho(2), Indah Soesanti(3)

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(*) Corresponding Author

Abstract


Cardiovascular disease has been the number one illness to cause death in the world for years. As information technology develops, many researchers have conducted studies on a computer-assisted diagnosis for heart disease. Predicting heart disease using a computer-assisted system can reduce time and costs. Feature selection can be used to choose the most relevant variables for heart disease. It includes filter, wrapper, embedded, and hybrid. The filter method excels in computation speed. The wrapper and embedded methods consider feature dependencies and interact with classifiers. The hybrid method takes advantage of several methods. Classification is a data mining technique to predict heart disease. It includes traditional machine learning, ensemble learning, hybrid, and deep learning. Traditional machine learning uses a specific algorithm. The ensemble learning combines the predictions of multiple classifiers to improve the performance of a single classifier. The hybrid approach combines some techniques and takes advantage of each method. Deep learning does not require a predetermined feature engineering. This research provides an overview of feature selection and classification methods for the prediction of heart disease in the last ten years. Thus, it can be used as a reference in choosing a method for heart disease prediction for future research.

Keywords


Feature Selection;Classification;Heart Disease

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References

A. Rodgers, C.M.M. Lawes, T. Gaziano, and T. Vos, “The Growing Burden of Risk from High Blood Pressure, Cholesterol, and Bodyweight,” in Disease Control Priorities in Developing Countries. 2nd ed., D.T. Jamison, J.G. Breman, A.R. Measham, G. Alleyne, M. Claeson, D.B. Evans, P. Jha, A. Mills, and P. Musgrove, Eds., New York, USA: Oxford University Press, 2006, pp. 851-868.

Y. Yan, J.W. Zhang, G.Y. Zang, and J. Pu, “The Primary use of Artificial Intelligence in Cardiovascular Diseases: What Kind of Potential Role Does Artificial Intelligence Play in Future Medicine?,” Journal of Geriatric Cardiology, Vol. 16, No. 2019, pp. 585-591, 2019.

D. Normawati and S. Winarti, “Feature Selection with Combination Classifier Use Rules-Based Data Mining for Diagnosis of Coronary Heart Disease,” 12th International Conference on Telecommunication Systems, Services, and Applications (TSSA), 2018, pp. 1-6.

J. Li, K. Cheng, S. Wang, F. Morstatter, R.P. Trevino, J. Tang, and H. Liu, “Feature Selection: A Data Perspective,” ACM Computing Surveys, Vol. 50, No. 6, pp.1-45, 2017.

N. Hoque, M. Singh, and D.K. Bhattacharyya, “EFS-MI: An Ensemble Feature Selection Method for Classification,” Complex and Intelligent System, Vol. 4, pp. 105-118, 2018.

G. Kesavaraj and S. Sukumaran, “A Study on Classification Techniques in Data Mining,” 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 2013, pp. 1-7.

D. Jain and V. Singh, “Feature Selection and Classification Systems for Chronic Disease Prediction: A Review,” Egyptian Informatics Journal, Vol. 19, No. 3, pp. 179-189, 2018.

I. Guyon and A. Elisseeff, “An Introduction of Variable and Feature Selection,” Journal of Machine Learning Research, Vol. 3, pp. 1157-1182, 2003.

G. Chandrashekar and F. Sahin, “A Survey on Feature Selection Methods,” Computers and Electrical Engineering, Vol. 40, No. 1, pp. 16-28, 2014.

S. Visalakshi and V. Radha, “A Literature Review of Feature Selection Techniques and Applications: Review of Feature Selection in Data Mining,” 2014 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, 2014, pp. 1-6.

M.W. Mwadulo, “A Review on Feature Selection Methods for Classification Tasks,” International Journal of Computer Applications Technology and Research, Vol. 5, No. 6, pp. 395-402.

J.C. Ang, A. Mirzal, H. Haron, and H.N.A. Hamed, “Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 13, No. 5, pp. 971-989, 2016.

A. Khempila and V. Boonjing, “Heart Disease Classification Using Neural Network and Feature Selection,” 21st International Conference on Systems Engineering, 2011, pp. 406-409.

H. Takci, “Improvement of Heart Attack Prediction by the Feature Selection Methods,” Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 26, No. 1, pp. 1-10, 2018.

S. Bashir, Z.S. Khan, F.H. Khan, A. Anjum, and K. Bashir, “Improving Heart Disease Prediction Using Feature,” 16th International Bhurban Conference on Applied Sciences & Technology (IBCAST), 2019, pp. 619-623.

A.M. Usman, U.K. Yusof, and S. Naim, “Cuckoo Inspired Algorithms for Feature Selection in Heart,” International Journal of Advances in Intelligent Informatics, Vol. 4, No. 2, pp. 95-106, 2018.

D.A.A.G. Singh, S.A. Balamurugan, and E.J. Leavline, “Literature Review on Feature Selection Methods for High-Dimensional Data,” International Journal of Computer Applications, Vol. 136, No. 1, pp. 9-17, 2016.

R.S. El-Sayed, “Linear Discriminant Analysis for an Efficient Diagnosis of Heart Disease via Attribute Filtering Based on Genetic Algorithm,” Journal of Computers, Vol. 13, No. 11, pp. 1290-1299, 2018.

J. Zeniarja, A. Ukhifahdhina, and A. Salam, “Diagnosis of Heart Disease Using K-Nearest Neighbor Method Based on Forward Selection,” Journal of Applied Intelligent System, Vol. 4, No. 2, pp. 39-47, 2019.

A.U. Haq, J. Li, M.H. Memon, M.H. Memon, J. Khan, and S.M. Marium, “Heart Disease Prediction System Using Model of Machine Learning and Sequential Backward Selection Algorithm for Features Selection,” 5th International Conference for Convergence in Technology (I2CT), 2019, pp. 1-4.

U.M. Khaire and R. Dhanalakshmi, “Stability of Feature Selection Algorithm: A. Review,” Journal of King Saud University – Computer and Information Sciences, pp. 1-14, 2019.

K. Rajeswari, V. Vaithiyanathan, and T.R. Neelakantan, “Feature Selection in Ischemic Heart Disease Identification using Feed Forward Neural Networks,” Procedia Engineering, Vol. 41, pp. 1818-1823, 2012.

V. Sabarinathan and V. Sugumaran, “Diagnosis of Heart Disease Using Decision Tree,” International Journal of Research in Computer Applications & Information Technology, Vol. 2, No. 6, pp. 74-79, 2014.

C. Yang, B. An, and S. Yin, “Heart-Disease Diagnosis via Support Vector Machine Based Approaches,” IEEE International Conference on Systems, Man, and Cybernetics, 2018, pp. 3153-3158.

H.-H. Hsu, C.-W. Hsieh, and M.-D. Lu, “Hybrid Feature Selection by Combining Filters and Wrappers,” Expert Systems with Applications, Vol. 38, No. 7, pp. 8144-8150, 2011.

J. Vijayashree and H.P. Sultana, “A Machine Learning Framework for Feature Selection in Heart Disease Classification Using Improved Particle Swarm Optimization with Support Vector Machine Classifier,” Program Comput Software, Vol. 44, pp. 388–397, 2018.

X. Liu, X. Wang, Q. Su, M. Zhang, Y. Zhu, Q. Wang, and Q. Wang, “A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method,” Computational and Mathematical Methods in Medicine, Vol. 2017, pp. 1-11, 2017.

K. Pahwa and R. Kumar, “Prediction of Heart Disease Using Hybrid Technique for Selecting Features,” 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer, and Electronics (UPCON), 2017, pp. 500-504.

S.C. Pandey, “Data Mining Techniques for Medical Data: A Review,” International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)-2016, 2016, pp. 972-982.

A.H. Seh and P.K. Chaurasia, “A Review on Heart Disease Prediction Using Machine Learning Techniques,” International Journal of Management, IT & Engineering, Vol. 9, No. 4, pp. 208-224, 2019.

A.S. Karthiga, M.S. Mary, and M. Yogasini, “Early Prediction of Heart Disease Using Decision Tree Algorithm,” International Journal of Advanced Research in Basic Engineering Sciences and Technology (IJARBEST), Vol. 3, No. 3, pp. 1-16, 2017.

K.U. Maheswari and J. Jasmie, “Neural Network-based Heart Disease Prediction,” International Journal of Engineering Research & Technology (IJERT), Vol. 5, No. 17, pp. 1-4, 2017.

U.N. Dulhare, “Prediction System for Heart Disease Using Naive Bayes and Particle Swarm Optimization,” Biomedical Research 2018, Vol. 29, No. 12, pp. 2646-2649, 2018.

A. Gupta, S. Yadav, S. Shahid, and V. U, “HeartCare: IoT based Heart Disease Prediction System,” 2019 International Conference on Information Technology (ICIT), 2019, pp. 88-93.

S. Pouriyeh, S. Vahid, G. Sannino, D.G. Pietro, H. Arabnia, and J. Gutierrez, “A Comprehensive Investigation and Comparison of Machine Learning Techniques in the Domain of Heart Disease,” 2017 IEEE Symposium on Computers and Communications (ISCC), 2017, pp. 1-4.

C.B.C. Latha and S.C. Jeeva, “Improving the Accuracy of Prediction of Heart Disease Risk Based on Ensemble Classification Techniques,” Informatics in Medicine Unlocked, Vol. 16, pp. 1-9, 2019.

A.P. Pawlovsky, “An Ensemble Based on Distances for a kNN Method for Heart Disease Diagnosis,” 2018 International Conference on Electronics, Information, and Communication (ICEIC), 2018, pp. 1-4.

А. Ed-daoudy and K. Mааlmi, “Реrfоrmаnсе Еvаluаtiоn оf Mасhinе Lеаrning bаsеd Big Dаtа Рrосеssing Frаmеwоrk fоr Рrеdiсtiоn оf Hеаrt Disease,” 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS), 2019, pp. 1-5.

S. Mohan, C. Thirumalai, and G. Srivastava, “Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques,” IEEE Access, Vol. 7, pp. 81542–81554, 2019.

V. Miškovic, “Machine Learning of Hybrid Classification Models for Decision Support,” SINTEZA 2014, 2014, pp. 318-323.

P. Kazienko, E. Lughofer, and B. Trawinski, “Editorial on the Special Issue ‘Hybrid and Ensemble Techniques in Soft Computing: Recent Advances and Emerging Trends’,” Soft Computing, Vol. 19, pp. 3353–3355, 2015.

M. Saini, N. Baliyan, and V. Bassi, “Prediction of Heart Disease Severity with Hybrid Data Mining,” 2017 2nd International Conference on Telecommunication and Networks (TEL-NET 2017), 2017, pp. 1-6.

E. Nikookar and E. Naderi, “Hybrid Ensemble Framework for Heart Disease Detection and Prediction,” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 5, pp. 243-248, 2018.

B.-B. Benuwa, Y. Zhan, B. Ghansah, D.K. Wornyo, and F.B. Kataka, “A Review of Deep Machine Learning,” International Journal of Engineering Research in Africa, Vol. 24, pp. 124-136, 2016.

A. Hernández-Blanco, B. Herrera-Flores, D. Tomás and B. Navarro-Colorado, “A Systematic Review of Deep Learning Approaches to Educational Data Mining,” Complexity, Vol. 2019, pp. 1-22, 2019.

B. Zohuri and M. Moghaddam, “Deep Learning Limitations and Flaws,” Modern Approaches on Material Science, pp. 241-250, 2020.

K.H. Miao and J.H. Miao, “Coronary Heart Disease Diagnosis using Deep Neural Networks,” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 10, pp. 1-8, 2018.

P. Anandajayam, C. Krishnakoumar, S. Vikneshvaran, and B. Suryanaraynan, “Coronary Heart Disease Predictive Decision Scheme Using Big Data and RNN,” Proceeding of International Conference on Systems Computation Automation and Networking 2019, 2019, pp. 1-6.

V. Shankar, V. Kumar, U. Devagade, V. Karanth, and K. Rohitaksha, “Heart Disease Prediction Using CNN Algorithm,” SN Computer Science, Vol. 1, pp. 1-8, 2020.



DOI: https://doi.org/10.22146/ijitee.59193

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