Predicting Heart Failure Status Using Binary Logistic Regression with Clinical and Demographic Factors: Penelitian

Nita Cahyani; Rahmat Irsyada

doi:10.31004/jerkin.v4i3.5189

Authors

Nita Cahyani Universitas Padjadjaran
Rahmat Irsyada Politeknik Negeri Subang

DOI:

https://doi.org/10.31004/jerkin.v4i3.5189

Keywords:

Heart Failure, Logistic Regression, Odds Ratio, ROC–AUC, Clinical Predictors

Abstract

The aim of this study was to identify clinical and demographic characteristics associated with heart failure and develop an interpretable risk model using binary logistic regression on hospital patient data. Early detection of heart failure is expected to support timely intervention and clinical decision-making based on routine measurements. This study analyzed 130 anonymized patient data with heart failure status as a binary outcome. The initial logistic regression model included all candidate predictors and was then simplified to improve stability and calibration. Results are presented as odds ratios with 95% CIs. Performance evaluation included ROC–AUC, classification metrics, the Hosmer–Lemeshow test, calibration plots, and 5-fold cross-validation. The final model was significant (LR p = 1.0×10⁻⁵; McFadden R² = 0.222) with an accuracy of 81.54%, sensitivity of 89.41%, specificity of 66.67%, AUC of 0.811, and a Brier score of 0.164. Cross-validation showed an average AUC of 0.774 and an accuracy of 0.762. Significant predictors included BMI, serum creatinine, serum potassium, and total cholesterol, with acceptable calibration (p = 0.0767). This model has potential use as an interpretive screening tool, although external validation is still needed.

References

Alba, A. C., Agoritsas, T., Walsh, M., Hanna, S., Iorio, A., Devereaux, P. J., … Guyatt, G. (2017). Discrimination and Calibration of Clinical Prediction Models: Users’ Guides to the Medical Literature. JAMA, 318(14), 1377–1384. Retrieved from https://doi.org/10.1001/jama.2017.12126

Arif, M., & Cahyani, N. (2022). Pemodelan Regresi Logistik Ordinal Pada Indeks Pembangunan Manusia ( IPM ) Di Jawa Timur Tahun 2020, 1(2), 64–73.

Aune, D., Sen, A., Norat, T., Janszky, I., Romundstad, P., Tonstad, S., & Vatten, L. J. (2016). Body Mass Index, Abdominal Fatness, and Heart Failure Incidence and Mortality. Circulation, 133(7), 639–649. Retrieved from https://doi.org/10.1161/CIRCULATIONAHA.115.016801

Buckley, L. F., Dorbala, P., Lamberson, V., Claggett, B. L., Ren, Y., Grams, M. E., … Shah, A. M. (2025). Linking Chronic Kidney Disease to Incident Heart Failure and Adverse Cardiac Remodeling Through the Plasma Proteome: The ARIC Study. JACC: Heart Failure, 13(8), 102512. Retrieved from https://doi.org/https://doi.org/10.1016/j.jchf.2025.102512

Cahyani, N., Fithriasari, K., & Iriawan, N. (2018). On the Comparison of Deep Learning Neural Network and Binary Logistic Regression for Classifying the Acceptance Status of Bidikmisi Scholarship Applicants in East Java Methods, 83–90.

Cahyani, N., & Irsyada, R. (2025). Performance Comparison of SelectKBest and Permutation Importance in Feature Selection for Diabetes Prediction, 5(1), 529–541.

Cahyani, N., Pangastuti, S., Fithriasari, K., Irhamah, I., & Iriawan, N. (2021). Classification of Bidikmisi Scholarship Acceptance using Neural Network Based on Hybrid Method of Genetic Algorithm. Indonesian Journal of Statistics and Its Applications, 5, 396–404. Retrieved from https://doi.org/10.29244/ijsa.v5i2p396-404

Calster, B. Van, Mclernon, D. J., Smeden, M. Van, Wynants, L., & Steyerberg, E. W. (2019). Calibration : the Achilles heel of predictive analytics, 1–7.

Carter, J. V, Pan, J., Rai, S. N., & Galandiuk, S. (2016). ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves. Surgery, 159(6), 1638–1645. Retrieved from https://doi.org/https://doi.org/10.1016/j.surg.2015.12.029

Collaboration, B. P. L. T. T. (2021). Pharmacological blood pressure lowering for primary and secondary prevention of cardiovascular disease across different levels of blood pressure: an individual participant-level data meta-analysis. Lancet (London, England), 397(10285), 1625–1636. Retrieved from https://doi.org/10.1016/S0140-6736(21)00590-0

Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Calster, B. Van, … Ordish, J. (2024). RESEARCH METHODS AND REPORTING TRIPOD + AI statement : updated guidance for reporting clinical prediction models that use regression or machine learning methods The TRIPOD ( Transparent Reporting of a multivariable prediction model for. Retrieved from https://doi.org/10.1136/bmj-2023-078378

Collins, G. S., Reitsma, J. B., & Altman, D. G. (2015). Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis ( TRIPOD ): the TRIPOD statement, 7594(January), 1–9. Retrieved from https://doi.org/10.1136/bmj.g7594

Ding, N., Shah, A. M., Blaha, M. J., Chang, P. P., Rosamond, W. D., & Matsushita, K. (2022). Cigarette Smoking, Cessation, and Risk of Heart Failure With Preserved and Reduced Ejection Fraction, 79(23). Retrieved from https://doi.org/10.1016/j.jacc.2022.03.377

Ettehad, D., Emdin, C. A., Kiran, A., Anderson, S. G., Callender, T., Emberson, J., … Rodgers, A. (2015). Blood pressure lowering for prevention of cardiovascular disease and death : a systematic review and meta-analysis. The Lancet, 6736(15), 1–11. Retrieved from https://doi.org/10.1016/S0140-6736(15)01225-8

Ferreira JP, Butler J, Rossignol P, D. (2020). Abnormalities of Potassium in Heart Failure, 75(22). Retrieved from https://doi.org/10.1016/j.jacc.2020.04.021

Heidenreich, P. A., Bozkurt, B., Aguilar, D., Allen, L. A., Colvin, M. M., Deswal, A., … Yancy, C. W. (2022). 2022 AHA / ACC / HFSA Guideline for the Management of Heart Failure. Retrieved from https://doi.org/10.1016/j.jacc.2021.12.012

Hosmer, D. W., & Lemesbow, S. (1980). Goodness of fit tests for the multiple logistic regression model. Communications in Statistics - Theory and Methods, 9(10), 1043–1069. Retrieved from https://doi.org/10.1080/03610928008827941

Huang, C.-C. L., Jou, Y.-J., & Cho, H.-J. (2016). A new multicollinearity diagnostic for generalized linear models. Journal of Applied Statistics, 43(11), 2029–2043. Retrieved from https://doi.org/10.1080/02664763.2015.1126239

Iriawan, N., Fithriasari, K., Ulama, B. S. S., Suryaningtyas, W., Pangastuti, S. S., Cahyani, N., & Qadrini, L. (2018). On The Comparison: Random Forest, SMOTE-Bagging, and Bernoulli Mixture to Classify Bidikmisi Dataset in East Java. In 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM) (pp. 137–141). Retrieved from https://doi.org/10.1109/CENIM.2018.8711035

Lachute, C. L., Seltz, J., Lavie, C. J., & Mandras, S. A. (2024). Obesity and Weight Loss Strategies for Patients With Heart Failure, 12(9). Retrieved from https://doi.org/10.1016/j.jchf.2024.06.006

Lala, A., Beavers, C., Blumer, V., Brewer, L., Oliveira-gomes, D. De, Dunbar, S., … Gulati, M. (2025). American Journal of Preventive Cardiology The continuum of prevention and heart failure in cardiovascular medicine : A joint scientific statement from the Heart Failure Society of America and the American Society for Preventive Cardiology. American Journal of Preventive Cardiology, 24(August), 101069. Retrieved from https://doi.org/10.1016/j.ajpc.2025.101069

Lee, H. (2019). Influence of Smoking Status on Risk of Incident Heart Failure : A Systematic Review and Meta-Analysis of Prospective Cohort Studies.

Polcwiartek, C., Hansen, S. M., Kragholm, K., Krogager, M. L., Aldahl, M., Køber, L., … Søgaard, P. (2018). Prognostic role of serum sodium levels across different serum potassium levels in heart failure patients: A Danish register-based cohort study. International Journal of Cardiology, 272, 244–249. Retrieved from https://doi.org/https://doi.org/10.1016/j.ijcard.2018.08.045

Rainio, O. (2024). Evaluation metrics and statistical tests for machine learning. Scientific Reports, 1–14. Retrieved from https://doi.org/10.1038/s41598-024-56706-x

Riley, R. D., Archer, L., Snell, K. I. E., Ensor, J., Dhiman, P., Martin, G. P., … Collins, G. S. (2024). Evaluation of clinical prediction models ( part 2 ): how to undertake an external validation study, (part 2), 1–12. Retrieved from https://doi.org/10.1136/bmj-2023-074820

Riley, R. D., Snell, K. I. E., Archer, L., Ensor, J., Debray, T. P. A., Calster, B. Van, … Collins, G. S. (2024). Evaluation of clinical prediction models ( part 3 ): calculating the sample size required for an external validation study, (part 3). Retrieved from https://doi.org/10.1136/bmj-2023-074821

Surjanovic, N., & Loughin, T. M. (2024). Improving the Hosmer-Lemeshow goodness-of-fit test in large models with replicated Bernoulli trials. Journal of Applied Statistics, 51(7), 1399–1411. Retrieved from https://doi.org/10.1080/02664763.2023.2272223

Wei, Y., Liu, S., Mu, Y., Liang, X., Chen, Z., Liu, Y., & Dong, G. (2025). Prediction models and risk scores in different types of heart failure : a review, (November). Retrieved from https://doi.org/10.3389/fmed.2025.1652307

Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., … Mallett, S. (2019). PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Annals of Internal Medicine, 170(1), 51–58. Retrieved from https://doi.org/10.7326/M18-1376

Yoo, J. E., Jeong, S.-M., Yeo, Y., Jung, W., Yoo, J., Han, K., … Shin, D. W. (2023). Smoking Cessation Reduces the Risk of Heart Failure: A Nationwide Cohort Study. JACC. Heart Failure, 11(3), 277–287. Retrieved from https://doi.org/10.1016/j.jchf.2022.07.006

Zelnick, L. R., Shlipak, M. G., Soliman, E. Z., Anderson, A., Christenson, R., Kansal, M., … Bansal, N. (2022). Prediction of Incident Heart Failure in CKD: The CRIC Study. Kidney International Reports, 7(4), 708–719. Retrieved from https://doi.org/10.1016/j.ekir.2022.01.1067

Zhang, Y., Golbus, J. R., Wittrup, E., Aaronson, K. D., & Najarian, K. (2024). Enhancing heart failure treatment decisions : interpretable machine learning models for advanced therapy eligibility prediction using EHR data. BMC Medical Informatics and Decision Making, 1–14. Retrieved from https://doi.org/10.1186/s12911-024-02453-y