2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 20 , Issue 2 , PP: 115-125, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Elda Maraj 1 , Aida Bendo 2 *
Doi: https://doi.org/10.54216/FPA.200210
Coronary heart disease, a prevalent cardiovascular condition, affects coronary arteries, causing progression over time. Factors include diabetes, hypertension, inactivity, and tobacco use. Treatment includes medications and surgery, while maintaining a balanced diet and regular physical activity can prevent it. This research aimed to develop and validate a predictive model for CHD occurrence, leveraging the power of multiple regression while considering a range of predisposing variables. This study uses a quantitative, retrospective design utilizing multiple regression analysis to predict the likelihood of coronary heart disease (CHD). The study involved 130 participants aged 24-85, with health history data on cardiovascular risk factors, blood pressure, cholesterol, smoking, BMI, and family history of heart disease. Multiple regression analysis was utilized to determine the significant predictors of CHD diagnosis. Significant relationships between responder variables and predictor factors in a multiple linear function are identified using multiple regression analysis. Our model discovered that a higher risk of coronary heart disease (CHD) was closely associated with both total cholesterol and BMI. The model included factors like systolic blood pressure, diabetes, physical activity, and smoking, but they had lower contributions to the prediction equation, despite cholesterol and BMI being the best predictors. This study successfully developed a multiple regression-based prediction model for CHD that can contribute to a more informed and potentially proactive approach to cardiac healthcare. Further work should focus on refining model accuracy and real-world clinical application.
Coronary heart disease , Multiple linear regression , Predictors , Explanatory model
[1] R. López-Osca, G. López-García, A. Granero-Gallegos, and M. Carrasco Poyatos, “Psychophysiological profile of ischemic patients according to their resting heart rate variability: one step closer to training individualization,” Retos, vol. 62, pp. 196–204, 2025. https://doi.org/10.47197/retos.v62.107794
[2] C. H. Chu, H. M. Shih, S. H. Yu et al., “Risk factors for sudden cardiac arrest in patients with ST-segment elevation myocardial infarction: a retrospective cohort study,” BMC Emerg. Med., vol. 22, p. 169, 2022. https://doi.org/10.1186/s12873-022-00732-3
[3] J. L. Liu, N. Maniadakis, A. Gray, and M. Rayner, “The economic burden of coronary heart disease in the UK,” Heart, vol. 88, no. 6, pp. 597–603, 2002. https://doi.org/10.1136/heart.88.6.597
[4] A. Henderson, “Coronary heart disease: Overview,” The Lancet, vol. 348, no. Suppl 1, pp. S1-S4, 1996. DOI: 10.1016/S0140-6736(96)98001-0
[5] G. M. Blue, E. P. Kirk, G. F. Sholler, R. P. Harvey, and D. S. Winlaw, “Congenital heart disease: current knowledge about causes and inheritance,” Med. J. Aust., vol. 197, no. 3, pp. 155–159, 2012. https://doi.org/10.5694/mja12.10811
[6] R. Hajar, “Risk Factors for Coronary Artery Disease: Historical Perspectives,” Heart Views, vol. 18, no. 3, pp. 109–114, 2017. https://doi.org/10.4103/HEARTVIEWS.HEARTVIEWS_106_17
[7] L. E. Chambless, C. P. Cummiskey, and G. Cui, “Several methods to assess improvement in risk prediction models: Extension to survival analysis,” Statist. Med., vol. 30, no. 1, pp. 22-38, 2010. https://doi.org/10.1002/sim.4026
[8] A. Bendo and F. Mara, “An Experimental Model on Multiple Regression Analysis in CMJ and SJ Jump Tests on 10-14 Years Old Players of Tirana Football Club,” Int. J. Hum. Mov. Sports Sci., vol. 8, no. 5, pp. 292-297, 2020. DOI: 10.13189/saj.2020.080518
[9] S. W. Lee, “Regression analysis for continuous independent variables in medical research: statistical standard and guideline of Life Cycle Committee,” Life Cycle, vol. 2, p. e3, pp. 1-8, 2022. https://doi.org/10.54724/lc.2022.e3
[10] E. Maraj and S. Kuka, “Prediction of Coronary Heart Disease Using Fuzzy Logic: Case Study in Albania,” in 2022 Int. Conf. Electr., Comput. Energy Technol. (ICECET), 2022, pp. 1-6. DOI: 10.1109/ICECET55527.2022.9872569
[11] A. Bendo and F. Brovina, “A statistical model using multiple regression analysis to predict equilibrium and sway index,” J. Phys. Educ. Sport, vol. 24, no. 6, pp. 1446-1456, 2024. DOI:10.7752/jpes.2024.06164
[12] Q. Kecheng, “Research on linear regression algorithms,” MATEC Web Conf., vol. 395, p. 01046, 2024. https://doi.org/10.1051/matecconf/202439501046
[13] H. Habehh and S. Gohel, “Machine Learning in Healthcare,” Curr. Genomics, vol. 22, no. 4, pp. 291–300, 2021. https://doi.org/10.2174/1389202922666210705124359
[14] M. Azhari and F. Fitriani, “Coronary Heart Disease Risk Prediction Using Binary Logistic Regression Based on Principal Component Analysis,” ENTHUSIASTIC Int. J. Stat. Data Sci., vol. 2, no. 1, pp. 47-55, 2022. https://doi.org/10.20885/enthusiastic.vol2.iss1.art6
[15] B. Kumar and U. Priyadarsini, “Accuracy Analysis of Heart Disease Prediction using Logistic Regression in Comparison with the Linear Regression Algorithm,” J. Pharm. Neg. Results, vol. 13, no. 4, pp. 1666-1672, 2022. https://doi.org/10.47750/pnr.2022.13.S04.199
[16] S. Darroudi et al., “Multivariate linear regression to predict association of non-invasive arterial stiffness with cardiovascular events,” ESC Heart Fail, pp. 1-10, 2024. https://doi.org/10.1002/ehf2.15077
[17] A. Mohanavel and J. Mathew, “Heart Disease Analysis using Multiple Linear Regression,” Int. J. Eng. Res. Technol., vol. 10, no. 9, pp. 718-721, 2021. DOI: 10.17577/IJERTV10IS090248
[18] V. Chavan, N. Doaj, and N. Vaswani, “Predicting Coronary Heart Disease using Various Regression Analysis,” Int. J. Innov. Sci. Res. Technol., vol. 9, no. 3, pp. 2872-2877, 2024. DOI: https://doi.org/10.38124/ijisrt/IJISRT24MAR2107
[19] N. Azdaki et al., “Which risk factor best predicts coronary artery disease using artificial neural network method?,” BMC Med. Inform. Decis. Mak., pp. 1-12, 2024. https://doi.org/10.1186/s12911-024-02442-1
[20] S. Tao et al., “Development and validation of a clinical prediction model for detecting coronary heart disease in middle-aged and elderly people: a diagnostic study,” Eur. J. Med. Res., pp. 1-13, 2023. https://doi.org/10.1186/s40001-023-01233-0
[21] H. Wu, “Using logistic regression model to predict the future coronary heart disease,” Theor. Nat. Sci., vol. 50, pp. 83-90, 2024. DOI:10.54254/2753-8818/50/2024AU0147
[22] J. A. A. G. Damen et al., “Prediction models for cardiovascular disease risk in the general population: systematic review,” BMJ, vol. 353, p. i2416, 2016. doi: https://doi.org/10.1136/bmj.i2416
[23] A. Bitton and T. A. Gaziano, “The Framingham Heart Study's impact on global risk assessment,” Prog. Cardiovasc. Dis., vol. 53, no. 1, pp. 68–78, 2010. https://doi.org/10.1016/j.pcad.2010.04.001
[24] P. W. Wilson et al., “Prediction of coronary heart disease using risk factor categories,” Circulation, vol. 97, no. 18, pp. 1837–1847, 1998. https://doi.org/10.1161/01.cir.97.18.1837
[25] B. Ismail and M. Anil, “Regression methods for analysing the risk factors for a lifestyle disease among the young population of India,” Indian Heart J., vol. 66, no. 6, pp. 587–592, 2014. https://doi.org/10.1016/j.ihj.2014.05.027
[26] S. R. Mirjalili et al., “An innovative model for predicting coronary heart disease using triglyceride-glucose index: a machine learning-based cohort study,” Cardiovasc. Diabetol, vol. 22, p. 200, 2023. https://doi.org/10.1186/s12933-023-01939-9
[27] A. Ogunpola, F. Saeed, S. Basurra, A. M. Albarrak, and S. N. Qasem, “Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases,” Diagnostics, vol. 14, no. 2, p. 144, 2024. https://doi.org/10.3390/diagnostics14020144
[28] L. A. Ali et al., “An Automated Diagnostic System for Heart Disease Prediction Based on χ2 Statistical Model and Optimally Configured Deep Neural Network,” IEEE Access, vol. 7, pp. 35 623-35 631, 2019. DOI:10.1109/ACCESS.2019.2904800
