COMPARISON OF MACHINE LEARNING MODELS IN HEART FAILURE PREDICTION AND THEIR INTEGRATION INTO CLINICAL DECISION SUPPORT SYSTEMS

Öz

Heart failure remains a leading cause of morbidity and mortality worldwide, necessitating advanced tools for early risk prediction. This study presents an interactive, machine learning-driven web application designed to predict heart failure outcomes using clinical data. Leveraging the heart failure clinical records dataset (n=299), the application integrates a comprehensive suite of fifteen diverse predictive models, encompassing traditional/statistical-based algorithms, instance-based and probabilistic methods, various tree-based and ensemble techniques, and neural networks within an intuitive Shiny framework. Key features include exploratory data analysis (correlation matrices, feature importance), model training, and real-time risk prediction with customizable patient parameters. The system employs stratified cross-validation (10-fold) for robust evaluation and achieves impressive performance, with top-performing models exhibiting test set Area Under Curve values exceeding 0.85, alongside high scores in accuracy, sensitivity, specificity, and F1-score. By combining clinical variables such as ejection fraction, serum creatinine, and follow-up time, the tool demonstrates how interactive machine learning platforms can enhance clinical decision-making. The open-source R-Shiny implementation provides immediate visual feedback, model interpretability features, and a template for extending predictive analytics to other medical domains. This work bridges the gap between statistical modeling and clinical application, offering both a prognostic tool and an educational resource for data-driven cardiology.

Anahtar Kelimeler

• Heart Failure Prediction
• Machine Learning
• Clinical Decision Support
• R-Shiny.

Kaynakça

1. 1. McDonagh, T. A. et al., “2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure,” Eur. Heart J., Vol. 42, Issue. 36, Pages. 3599–3726, 2021.
2. 2. L. S. Ranard, S. A. Parikh, and A. J. Kirtane, “COVID-19–Specific Strategies for the Treatment of ST-Segment Elevation Myocardial Infarction in China,” J. Am. Coll. Cardiol., Vol. 76, Issue. 11, Pages. 1325–1327, 2020.
3. 3. Salman, O. K. M. and Aksoy, B. “Rasgele Orman Ve İki̇li̇ Parçacik Sürü Zekâsi Yöntemi̇yle Kalp Yetmezli̇ği̇ Hastalığındaki̇ Ölüm Ri̇ski̇ni̇n Tahmi̇nlenmesi̇,” Int. J. 3D Print. Technol. Digit. Ind., Vol. 6, Issue. 3, Pages. 416–428, 2022.
4. 4. Yapıcı, İ. Ş., Arslan, R. U., and Erkaymaz, O. “Kalp Yetmezliği Tanılı Hastaların Hayatta Kalma Tahmininde Topluluk Makine Öğrenme Yöntemlerinin Performans Analizi,” Karaelmas Fen ve Mühendislik Derg., Vol. 14, Issue. 1, Pages. 59–69, 2024.
5. 5. Erdaş, B., and Ölçer, D., “Kalp Yetmezliği Hastalarının Hayatta Kalma Tespiti İçin Makine Öğrenmesi Tabanlı Bir Yaklaşım,” Pages. 16–19, 2020.
6. 6. Keser, S. B., and Keskin, K., “Kalp Yetmezliği Hastalarının Sağ Kalım Tahmini: Sınıflandırmaya Dayalı Makine Öğrenmesi Algoritmalarının Bir Uygulaması,” Afyon Kocatepe Univ. J. Sci. Eng., Vol. 23, Issue. 2, Pages. 362–369, 2023.
7. 7. Winger, T., Ozdemir, C., Narasimhan, S. L. and Srivastava, J., “Time-Adaptive Machine Learning Models for Predicting the Severity of Heart Failure with Reduced Ejection Fraction,” Diagnostics, Vol. 15, Issue. 6, Pages. 1–11, 2025.
8. 8. Aydemir, M., Çakir, M., Oral, O., and Yilmaz, M., “Diagnosis of Cushing’s syndrome with generalized linear model and development of mobile application,” Medicine (Baltimore)., Vol. 104, Issue. 25, Pages. e42910, 2025.

9. 9. Kaba G., and Kalkan, S. B., “Kardiyovasküler Hastalık Tahmininde Makine Öğrenmesi Sınıflandırma Algoritmalarının Karşılaştırılması,” İstanbul Ticaret Üniversitesi Fen Bilim. Derg., Vol. 21, Issue. 42, Pages. 183–193, 2022.
10. 10. Gürgen, G., and Serttaş, S., “Kalp Yetmezliği Hastalığının Erken Teşhisinde Makine Öğrenimi Algoritmalarının Performans Karşılaştırması,” Euroasia J. Math. Eng. Nat. Med. Sci., Vol. 10, Pages. 165–174, 2023.
11. 11. Nasution, N., Nasution, F. B., and Hasan, M. A., “Predicting Heart Disease Using Machine Learning: An Evaluation of Logistic Regression, Random Forest, SVM, and KNN Models on the UCI Heart Disease Dataset,” IT J. Res. Dev., Vol. 9, Issue. 2, Pages. 140–150, 2025.
12. 12. Yang, M., Zhang, X., Fang, W., Zhang, Y., and Fan, X., “Trajectories and Predictors of Frailty in Patients with Heart Failure: A Longitudinal Study,” J. Clin. Nurs., Vol. 0, Pages. 1–14, 2025.
13. 13. Ribeiro, E. G. et al., “Effect of Telemedicine Interventions on Heart Failure Hospitalizations: A Randomized Trial,” J. Am. Heart Assoc., Vol. 14, Issue. 6, p. e036241, 2025.
14. 14. Qureshi, M., Ishaq, K., Daniyal, M., Iftikhar, H., Rehman, M. Z., and Salar, S. A. A., “Forecasting cardiovascular disease mortality using artificial neural networks in Sindh, Pakistan,” Springer, Vol. 25, Issue 1, 2025.
15. 15. Mondal, S., Maity, R., Reports, A. N.-S., “An efficient artificial neural network-based optimization techniques for the early prediction of coronary heart disease: comprehensive analysis,” nature.com, Vol. 15, Issue. 4827, Pages. 1–24, 2025.
16. 16. Escobar, D. A. C., and Rivera, M. M., “Prediction of Heart Disease Using a Pattern Recognition Approach with Feature Selection and Naïve Bayesian Classifier,” Springer, Pages. 347–363, 2025.
17. 17. Chitta, S., Chandu, S., Chaitanya, K., Katha, Sundar Junapudi, S., Kumar Yata, V. and Junapudi, S., “Clinical and demographic predictors of heart failure outcomes: A machine learning perspective,” Eurasian J. Med. Oncol., Vol. 9, Issue. 1, Page. 133, Jan. 2025.
18. 18. Chulde-Fernández, B., et al., “Classification of Heart Failure Using Machine Learning: A Comparative Study,” MDPI:Life, Vol. 15, Issue. 3, Pages. 1–18, 2025.
19. 19. Khalid Hussain, M., Wani, S., and Abubakar, A., “Examining Mortality Risk Prediction Using Machine Learning in Heart Failure Patients,” Int. J. Perceptive Cogn. Comput., Vol. 11, Issue. 1, Pages. 81–87, Jan. 2025.
20. 20. Akbulut, B., Çakır, M., Görkem Sarıkaya, M., Oral, O., Yılmaz, M., and Aykal, G., “Artificial intelligence to predict biomarkers for new-onset atrial fibrillation after coronary artery bypass grafting,” Turkish J. Thorac. Cardiovasc. Surg., Vol. 33, Issue. 2, Pages. 144–153, Apr. 2025.
21. 21. Atacak, İ., “Kalp Yetmezliği Tahmininin Kategorik Olarak Farklı Tip Makine Öğrenmesi Yöntemleri ile Uygulanmasına Yönelik Bir Değerlendirme Çalışması,” EMO Bilim. Dergi, Vol. 14, Issue. 1, Pages. 73–95, 2024.
22. 22. Yigit, T., Isik, A. H., and Ince, M., “Web‐based learning object selection software using analytical hierarchy process,” IET Softw., Vol. 8, Issue. 4, Pages. 174–183, 2014.
23. 23. Turan, T., Turan, G., and Köse, U., “Uyarlamalı Ağ Tabanlı Bulanık Mantık Çıkarım Sistemi ve Yapay Sinir Ağları ile Türkiye’deki COVID-19 Vefat Sayısının Tahmin Edilmesi,” Bilişim Teknol. Derg. Vol. 15, Issue. 2, Pages. 97–105, 2022.
24. 24. Almazroi, A. A., Aldhahri, E. A., Bashir, S., and Ashfaq, S., “A Clinical Decision Support System for Heart Disease Prediction Using Deep Learning,” IEEE Access, Vol. 11, Issue June, Pages. 61646–61659, 2023.
25. 25. Cox, D. R., “The Regression Analysis of Binary Sequences,” J. R. Stat. Soc. Ser. B Stat. Methodol., Vol. 20, Issue. 2, Pages. 215–232, 1958.
26. 26. Fisher, R. A., “The Use of Multiple Measurements in Taxonomic Problems,” Ann. Eugen., Vol. 7, Issue. 2, Pages. 179–188, 1936.
27. 27. Hastie, T., Tibshirani, R., and Friedman, J., The Elements of Statistical Learning. New York, NY: Springer New York, 2009.
28. 28. Fix E., and Hodges, J. L., “Discriminatory analysis. Nonparametric discrimination: Small sample performance,” 1951.
29. 29. Cover T., and Hart, P., “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory, Vol. 13, Issue. 1, Pages. 21–27, 1967.
30. 30. Maron, M. E., “Automatic Indexing: An Experimental Inquiry,” J. ACM, Vol. 8, Issue. 3, Pages. 404–417, 1961.
31. 31. Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J., Classification And Regression Trees. Routledge, 2017.
32. 32. Breiman, L., “Random forests,” Mach. Learn., Vol. 45, Issue. 1, Pages. 5–32, 2001.
33. 33. Breiman, L., “Bagging predictors,” Mach. Learn., Vol. 24, Issue. 2, Pages. 123–140, Aug. 1996.
34. 34. Wright, M. N., and Ziegler, A., “ranger : A Fast Implementation of Random Forests for High Dimensional Data in C++ and R,” J. Stat. Softw., Vol. 77, Issue. 1, 2017.
35. 35. Schapire, R. E., “The strength of weak learnability,” Mach. Learn., Vol. 5, Issue. 2, Pages. 197–227, Jun. 1990.
36. 36. Freund, Y., and Schapire, R. E., “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,” J. Comput. Syst. Sci., Vol. 55, Issue. 1, Pages. 119–139, Aug. 1997.
37. 37. Friedman, J. H., “Greedy function approximation: A gradient boosting machine,” Ann. Stat., Vol. 29, Issue. 5, Oct. 2001.
38. 38. Vapnik, V. N., The Nature of Statistical Learning Theory. New York, NY: Springer New York, 1995.
39. 39. Rumelhart, D. E., Hinton, G. E., and Williams, R. J., “Learning representations by back-propagating errors,” Nature, Vol. 323, Issue. 6088, Pages. 533–536, Oct. 1986.
40. 40. Chen, T., and Guestrin, C., “XGBoost,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, Vol. 13-17-Augu, Pages. 785–794. 41. Zou, H., and Hastie, T., “Regularization and variable selection via the elastic net,” J. R. Stat. Soc. Ser. B Stat. Methodol., Vol. 67, Issue. 2, Pages. 301–320, 2005.