Analysis of patient data to explore cardiovascular risk factors

Year 2024, , 133 - 148, 30.06.2024

Jawaher Almushayqih Abayomi Samuel Oke , Belindar Atieno Juma

https://doi.org/10.53391/mmnsa.1412304

Cited By: 1

Abstract

According to the World Health Organisation, cardiovascular diseases claim over 17.9 million lives yearly on a global scale. Hence, cardiovascular diseases are responsible for 32 percent of global deaths yearly. Furthermore, it is estimated that more than 50 percent of heart disease cases are only discovered after they have reached the critical stage of heart failure and stroke. However, early detection of these heart diseases can reduce the mortality rates of cardiovascular diseases. Scientists have suggested using machine learning algorithms to identify the risk factors. However, the unavailability of data has hindered the significant success of this approach. In this study, machine learning algorithms are used to identify the important features that should be monitored to prevent heart diseases by considering a dataset obtained from 1000 patients. The six machine learning algorithms used for this study are Logistic Regression, Support Vector Machine, k-nearest Neighbour, Decision Tree, Random Forest and Multi-layer Perception Classifier. The dataset consists of twelve features that are considered to be associated with heart disease and a target variable. The results from this study show that patients suffering from typical and atypical angina chest pain are prone to heart disease. Patients who exercise up the slope have a higher likelihood of living without heart disease. Among the six algorithms used, the MLP Multi-layer Perception Classifier outperforms all others by achieving a 99 percent accuracy. Moreover, the Random Forest algorithm follows with an accuracy of 98 percent.

Keywords

Machine learning algorithms, cardiovascular diseases, heart disease, risk factors

References

• [1] WHO, Cardiovascular diseases, (2023). https://www.who.int/health-topics/cardiovascular-diseases.
• [2] Allen, L.A., Stevenson, L.W., Grady, K.L., Goldstein, N.E., Matlock, D.D., Arnold, R.M. et al. Decision making in advanced heart failure: a scientific statement from the American Heart Association. Circulation, 125(15), 1928–1952, (2012).
• [3] Mori, S., Tretter, J.T., Spicer, D.E., Bolender, D.L. and Anderson, R.H. What is the real cardiac anatomy? Clinical Anatomy, 32(3), 288–309, (2019).
• [4] Buijtendijk, M.F.J., Barnett, P. and Van Den Hoff, M.J.B. Development of the human heart. American Journal of Medical Genetics Part C: Seminars in Medical Genetics, 184(1), 7-22, (2020).
• [5] Gumpangseth, T., Mahakkanukrauh, P. and Das, S. Gross age-related changes and diseases in human heart valves. Anatomy & Cell Biology, 52(1), 25-33, (2019).
• [6] Gumpangseth, T., Lekawanvijit, S. and Mahakkanukrauh, P. Histological assessment of the human heart valves and its relationship with age. Anatomy & Cell Biology, 53(3), 261–271, (2020).
• [7] Niklason, L.E. and Lawson, J.H. Bioengineered human blood vessels. Science, 370(6513), (2020).
• [8] Padala, S.K., Cabrera, J.A. and Ellenbogen, K.A. Anatomy of the cardiac conduction system. Pacing and Clinical Electrophysiology, 44(1), 15–25, (2021).
• [9] Hochman-Mendez, C., Mesquita, F.C.P., Morrissey, J., Da Costa, E.C., Hulsmann, J., TangQuan, K. et al. Restoring anatomical complexity of a left ventricle wall as a step toward bioengineering a human heart with human induced pluripotent stem cell-derived cardiac cells. Acta Biomaterialia, 141, 48–58, (2022).
• [10] Khan, M.A.B., Hashim, M.J., Mustafa, H., Baniyas, M.Y., Al Suwaidi, S.K.B.M., AlKatheeri, R. et al. Global epidemiology of ischemic heart disease: results from the global burden of disease study. Cureus, 12(7), (2020).
• [11] Khairy, P. Arrhythmias in adults with congenital heart disease: what the practicing cardiologist needs to know. Canadian Journal of Cardiology, 35(12), 1698–1707, (2019).
• [12] Shah, D., Patel, S. and Bharti, S.K. Heart disease prediction using machine learning techniques. SN Computer Science, 1, 345, (2020).
• [13] Saboor, A., Usman, M., Ali, S., Samad, A., Abrar, M.F. and Ullah, N. A method for improving prediction of human heart disease using machine learning algorithms. Mobile Information Systems, 2022(1), 1410169, (2022).
• [14] Ramesh, T.R., Lilhore, U.K., Poongodi, M., Simaiya, S., Kaur, A. and Hamdi, M. Predictive analysis of heart diseases with machine learning approaches. Malaysian Journal of Computer Science, 132–148, (2022).
• [15] Chang, V., Bhavani, V.R., Xu, A.Q. and Hossain, M.A. An artificial intelligence model for heart disease detection using machine learning algorithms. Healthcare Analytics, 2, 100016, (2022).
• [16] Boukhatem, C., Youssef, H.Y. and Nassif, A.B. Heart disease prediction using machine learning. In Proceedings, 2022 Advances in Science and Engineering Technology International Conferences (ASET), pp. 1-6, Dubai, United Arab Emirates, (2022, February).
• [17] Ahmadini, A.A.H. A novel technique for parameter estimation in intuitionistic fuzzy logistic regression model. Ain Shams Engineering Journal, 13(1), 101518, (2022).
• [18] José R. Berrendero, Beatriz Bueno-Larraz, and Antonio Cuevas. On functional logistic regression: some conceptual issues. Test, 32, 321-349, (2023).
• [19] Joshi, A.V. Support vector machines. In Machine Learning and Artificial Intelligence (pp. 89–99). Cham, Switzerland: Springer International Publishing, (2023).
• [20] Nino-Adan, I., Landa-Torres, I., Portillo, E. and Manjarres, D. Influence of statistical feature normalisation methods on K-Nearest Neighbours and K-Means in the context of industry 4.0. Engineering Applications of Artificial Intelligence, 111, 104807, (2022).
• [21] Meng, L., Bai, B., Zhang, W., Liu, L. and Zhang, C. Research on a decision tree classification algorithm based on granular matrices. Electronics, 12(21), 4470, (2023).
• [22] Bai, J., Li, Y., Li, J., Yang, X., Jiang, Y. and Xia, S.T. Multinomial random forest. Pattern Recognition, 122, 108331, (2022).
• [23] Al Bataineh, A., Kaur, D. and Jalali, S.M.J. Multi-layer perceptron training optimization using nature inspired computing. IEEE Access, 10, 36963–36977, (2022).
• [24] Cubukcu, A., Murray, I. and Anderson, S. What’s the risk? Assessment of patients with stable chest pain. Echo Research & Practice, 2(2), 41–48, (2015).