Support Vector Machines and Ensemble Methods for Improved Heart Disease Classification
Main Article Content
Abstract
Computing power and methodological advancements have brought to a significant deal of variety in the medical sciences, particularly in the area of human cardiac disease diagnosis. It has devastating consequences on human life and is now one of the most severe cardiac disorders in humans. Accurate and timely diagnosis of human cardiac sickness may significantly improve the patient's survival rate and prevent heart failure in its early stages. Manual techniques of diagnosing heart disease are prone to bias and examiner variability. When it comes to identifying and classifying individuals with heart disease from those without, machine learning algorithms are dependable and efficient resources. As per the suggested study, we used the heart disease dataset to analyze the performance of the machine learning algorithms with the help of different metrics, including sensitivity, specificity, F-measure, and classification accuracy. The algorithms were used to determine and forecast human cardiac disease. To do it, we first applied nine different machine learning classifiers to the final dataset (AB, LR, ET, MNB, CART, SVM, LDA, RF, and XGB) and compared their results before and after the change in the hyper parameter. We also do specific pre-processing, dataset standardization, and hyper parameter tweaking to ensure their correctness on the reference dataset for heart disease. We also used the industry-standard ML algorithms are trained and validated using the K-fold cross-validation approach. Finally, the results of the experiment demonstrated that by standardizing data and hyper-tuning the parameters of machine learning classifiers, the prediction classifiers' accuracy increased.
Article Details
Section
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
1. V. Manikantan and S. Latha, “Predicting the analysis of heart disease symptoms using medicinal data mining methods”, International Journal of Advanced Computer Theory and Engineering, vol. 2, pp.46-51, 2013.
2. Sellappan Palaniappan and Rafiah Awang, “Intelligent heart disease prediction system using data mining techniques”, International Journal of Computer Science and Network Security, vol.8, no.8 pp. 343-350,2008.
3. K.Srinivas, Dr.G.Ragavendra and Dr. A. Govardhan,“ A Survey on prediction of heart morbidity using data mining techniques”,International Journal of Data Mining & Knowledge Management Process (IJDKP) vol.1, no.3, pp.14-34, May 2011.
4. G.Subbalakshmi, K.Ramesh and N.Chinna Rao,“ Decision support in heart disease prediction system using Naïve Bayes”, ISSN: 0976-5166, vol. 2, no. 2.pp.170-176, 2011.
5. T.John Peter , K. Somasundaram, “An Empirical Study on Prediction of Heart Disease using classification data mining technique” IEEEInternational Conference On Advances In Engineering, Science And Management (ICAESM - 2012) March 30, 31, 2012.
6. Shamsher Bahadur Patel, Pramod Kumar Yadav and Dr. D. P.Shukla, “Predict the Diagnosis of Heart Disease Patients Using Classification Mining Techniques”,IOSR Journal of Agriculture and Veterinary Science (IOSR-JAVS),Volume 4, Issue 2 Jul. - Aug. 2013.
7. Niti Guru, Anil Dahiya, Navin Rajpal, "Decision Support System for Heart Disease Diagnosis Using Neural Network", Delhi Business Review, Vol. 8, No. I January - June 2007.
8. M.A.Nishara Banu, B.Gomathy, “Disease Forecasting System Using Data Mining Methods,” International Conference on Intelligent Computing Applications,2014
9. Feixiang Huang, Shengyong Wang, and ChienChung Chan, “Predicting Disease By Using Data Mining Based on Healthcare Information System”, IEEE International Conference on Granular Computing,2012.
10. Chotirat “Ann” Ratanamahatana and Dimitrios Gunopulos,“Scaling up the Naive Bayesian Classifier:Using Decision Trees for Feature Selection”, Computer Science Department University of California Riverside, CA 92521 1-909-787-5190
11. S. Goel, A. Deep, S. Srivastava, and A. Tripathi, ‘‘Comparative anal- ysis of various techniques for heart disease prediction,’’ in Proc. 4th Int. Conf. Inf. Syst. Comput. Netw. (ISCON), Mathura, India, Nov. 2019, pp. 88–94
12. A. Lakshmanarao, Y. Swathi, and P. S. S. Sundareswar, ‘‘Machine learning techniques for heart disease prediction,’’ Int. J. Sci. Technol. Res., vol. 8, no. 11, p. 97, Nov. 2019.
13. S. Mohan, C. Thirumalai, and G. Srivastava, ‘‘Effective heart disease prediction using hybrid machine learning techniques,’’ IEEE Access, vol. 7,pp. 81542–81554, 2019.
14. A. K. Gárate-Escamila, A. Hajjam El Hassani, and E. Andrès, ‘‘Classification models for heart disease prediction using feature selection and PCA,’’ Informat. Med. Unlocked, vol. 19, Jan. 2020, Art. no. 100330.
15. D. W. Hosmer, S. Lemeshow, and E. D. Cook, Applied Logistic Regression, 2nd ed. New York, NY, USA: Wiley, 2000.
16. E. Nasarian, M. Abdar, M. A. Fahami, R. Alizadehsani, S. Hussain, M. E. Basiri, M. Zomorodi- Moghadam, X. Zhou, P. Pławiak, U. R. Acharya, R.-S. Tan, and N. Sarrafzadegan, ‘‘Association between work-related features and coronary artery disease: A heterogeneous hybrid feature selection integrated with balancing approach,’’ Pattern Recognit. Lett., vol. 133, pp. 33–40, May 2020
17. R. Atallah and A. Al-Mousa, ‘‘Heart disease detection using machine learning majority voting ensemble method,’’ in Proc. 2nd Int. Conf. new Trends Comput. Sci. (ICTCS), Oct. 2019, pp. 1–6.
18. A. Gupta, L. Kumar, R. Jain, and P. Nagrath, ‘‘Heart disease pre-diction using classification (naive bayes),’’ in Proc. 1st Int. Conf. Comput., Commun., Cyber-Secur. (ICS). Singapore: Springer, 2020, pp. 561–573.
19. S. Mohan, C. thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning tech-niques,” IEEE Access, vol. 7, pp. 81542–81554, 2019.
20. N. K. Kumar, G. S. Sindhu, D. K. Prashanthi, andA. S. Sulthana, “Analysis and prediction of cardio vascular disease using machine learning classifiers,” in Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 15–21,IEEE, Coimbatore, India, 2020