Improved Sensitivity and Reliability in Heart Disease Survival Prediction: A Stacking SVM and Logistic Regression Approach
Keywords:
Support Vector Machine (SVM), Sample size, cardiovascular disease (CVD), Logistic Regression Performance metric, Stacked Ensemble Learning.Abstract
Predicting heart disease survival remains challenging in clinical data analysis due to the complexity and variability of the data sets. This research examines the performance of support vector machine (SVM) and logistic regression (LR) classifiers on a stroke prediction dataset to enhance sensitivity and reliability in heart disease prediction in varying sample sizes. Our analysis shows that SVM is consistently high in precision, specificity, and accuracy, while LR is variable, with a marked drop in sensitivity with increasing sample size. We propose a stacked ensemble model by integrating the strengths of SVM and LR. The stacked ensemble performs best in achieving the highest sensitivity of 0.97, specificity of 0.95, and F1-score of 0.96 in the largest sample size. This method significantly improves prediction accuracy and reliability, which makes it very applicable to early detection and effective management of heart disease.
Downloads
References
S. Jain and A. Saha, “Rank-based univariate feature selection methods on machine learning classifiers for code smell detection,” Evolutionary Intelligence, Vol. 15, no. 1, 2022, pp. 609–638.
Y. Liu, Y. Zhou, S. Wen, and C. Tang, “A strategy on selecting performance metrics for classifier evaluation,” International Journal of Mobile Computing and Multimedia Communications (IJMCMC), Vol. 6, no. 4, 2014, pp.20–35.
A. L. Bui, T. B. Horwich, and G. C. Fonarow, “Epidemiology and risk profile of heart failure,” Nature Reviews Cardiology, Vol. 8, no. 1, 2011, pp.30–41.
J. Mourao-Miranda, A. L. Bokde, C. Born, H. Hampel, and M. Stetter, “Classifying brain states and determining the discriminating activation patterns: support vector machine on functional mri data,” Neuroimage, Vol. 28, no. 4, 2005, pp. 980–995.
S. Ghwanmeh, A. Mohammad, and A. Al-Ibrahim, “Innovative artificial neural networks-based decision support system for heart diseases diagnosis,” 2013.
J. Soni, U. Ansari, D. Sharma, S. Soni et al., “Predictive data mining for medical diagnosis: An overview of heart disease prediction,” International Journal of Computer Applications, Vol. 17, no. 8, 2011, pp. 43–48.
H. D. Masethe and M. A. Masethe, “Prediction of heart disease using classification algorithms,” in Proceedings of the World Congress on Engineering and Computer Science, Vol. 2, no. 1, 2014, pp. 25–29.
K. Patterson, “Matthiasnahrendorf: Healing insights,” Circulation Research, Vol. 119, no. 7, 2016, pp. 790–793. 15
M. N. R. Chowdhury, E. Ahmed, M. A. D. Siddiq, and A. U. Zaman, “Heart disease prognosis using machine learning classification techniques,” in 2021 6th International Conference for Convergence in Technology (I2CT). IEEE, 2021, pp. 1–6.
S. Ware, S. Rakesh, and B. Choudhary, “Heart attack prediction by using machine learning techniques,” no, Vol. 5, 2020, pp. 1577–1580.
J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan, and A. Saboor, “Heart disease identification method using machine learning classification in healthcare,” IEEE Access, Vol. 8, 2020, pp. 107 562–107 582.
S. N. Khan, N. M. Nawi, A. Shahzad, A. Ullah, M. F. Mushtaq, J. Mir, and M. Aamir, “Comparative analysis for heart disease prediction,” JOIV: International Journal on Informatics Visualization, Vol. 1, no. 4-2, 2017,pp. 227–231.
A. Lakshmanarao, Y. Swathi, and P. S. S. Sundareswar, “Machine learning techniques for heart disease prediction,” Forest, Vol. 95, no. 99, 2019, p. 97.
K. Hariharan, W. Vigneshwar, N. Sivaramakrishnan, and V. Subramaniyaswamy, “A comparative study on heart disease analysis using classification techniques,” International Journal of Pure and Applied Mathematics, Vol. 119, no. 12, 2018, pp. 13 357–13 366.
S. Nashif, M. R. Raihan, M. R. Islam, and M. H. Imam, “Heart disease detection by using machine learning algorithms and a real-time cardiovascular health monitoring system,” World Journal of Engineering and Technology, Vol. 6, no. 4, 2018, pp. 854–873.
Fede Soriano, “Stroke prediction dataset,” Kaggle,2023,https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset Accessed: 2024-06-18.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
