A Pharmacy System Integrated with A Machine Learning Algorithm for Cardiovascular Disease Prediction
Kumar Shwetabh 1*, F Rahman 1, Sushree Sasmita Dash 1
Journal of Angiotherapy 8(1) 1-9 https://doi.org/10.25163/angiotherapy.819491
Submitted: 26 November 2023 Revised: 23 January 2024 Published: 26 January 2024
Abstract
Cardiovascular diseases are widely acknowledged as highly challenging and contribute significantly to global fatalities. The widespread use of medications has strained healthcare systems worldwide. In this review, we describes a method, Machine Learning-based Cardio Vascular Disease Prediction (ML-CVDP), designed for accurately predicting cardiovascular illnesses. The model addresses issues like missing values using the mean substitution approach and tackles data imbalances with the Synthetic Minority Over-sampling Technique (SMOTE). It employs an ensemble of Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT), and Artificial Neural Networks (ANN) for categorization. The Feature Significance approach is then applied to select features. Experimental results demonstrate the strategy's superiority over single-modal techniques, achieving an accuracy of 93.2%, precision of 94.1%, specificity of 89.6%, sensitivity of 94.1%, Mean Squared Error (MSE) of 6.3%, and Root Mean Squared Error (RMSE) of 4.8%. The True Positive Rate (TPR) is 91.5%, and the False Positive Rate (FPR) is 92.5%.
Keywords: Cardiovascular Disease, Machine Learning, Pharmacy System, Diagnosis
References
Abdalrada, A.S., Abawajy, J., Al-Quraishi, T., Islam, S.M.S. (2022). Machine learning models for predicting co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study. J. Diabetes Metab. Disord. 21(1), 251-261.
https://doi.org/10.1007/s40200-021-00968-z
Azmi, J., Arif, M., Nafis, M. T., Alam, M. A., Tanweer, S., & Wang, G. (2022). A systematic review on machine learning approaches for cardiovascular disease prediction using medical big data. Medical Engineering & Physics, 105, 103825.
https://doi.org/10.1016/j.medengphy.2022.103825
Bays, H.E., Taub, P.R., Epstein, E., Michos, E.D., Ferraro, R.A., Bailey, A.L., Toth, P.P. (2021). Ten things to know about ten cardiovascular disease risk factors. Am J Prev Cardiol. 5, 100149.
https://doi.org/10.1016/j.ajpc.2021.100149
Campbell, N.R., Ordunez, P., Giraldo, G., Morales, Y.A.R., Lombardi, C., Khan, T., Varghese, C. (2021). WHO HEARTS: a global program to reduce cardiovascular disease burden: experience implementing in the Americas and opportunities in Canada. Can J Cardiol. 37(5), 744-755.
https://doi.org/10.1016/j.cjca.2020.12.004
Cheung, C.Y., Xu, D., Cheng, C.Y., Sabanayagam, C., Tham, Y.C., Yu, M., Wong, T.Y. (2021). A deep-learning system for assessing cardiovascular disease risk via the measurement of retinal-vessel caliber. Nat. Biomed. Eng. 5(6), 498-508.
https://doi.org/10.1038/s41551-020-00626-4
Cho, S.Y., Kim, S.H., Kang, S.H., Lee, K.J., Choi, D., Kang, S., Chae, I.H. (2021). Pre-existing and machine learning-based models for cardiovascular risk prediction. Sci. Rep. 11(1), 8886.
https://doi.org/10.1038/s41598-021-88257-w
Gao, X.Y., Amin Ali, A., Shaban Hassan, H., Anwar, E.M. (2021). Improving the accuracy for analyzing heart disease prediction based on the ensemble method. Complex. 2021, 1-10.
https://doi.org/10.1155/2021/6663455
Ghiasi, M.M., Zendehboudi, S. (2021). Application of decision tree-based ensemble learning in the classification of breast cancer. Comput. Biol. Med. 128, 104089.
https://doi.org/10.1016/j.compbiomed.2020.104089
Hsu, W., Warren, J. R., & Riddle, P. J. (2022). Medication adherence prediction through temporal modelling in cardiovascular disease management. BMC Medical Informatics and Decision Making, 22(1), 1-21.
https://doi.org/10.1186/s12911-022-02052-9
https://www.nhlbi.nih.gov/science/framingham-heart-study-fhs
Imtiaz, S.I., ur Rehman, S., Javed, A.R., Jalil, Z., Liu, X., Alnumay, W.S. (2021). DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network. Future Gener Comput Syst. 115, 844-856.
https://doi.org/10.1016/j.future.2020.10.008
Jiang, H., Mao, H., Lu, H., Lin, P., Garry, W., Lu, H., Chen, X. (2021). Machine learning-based models to support decision-making in emergency department triage for patients with suspected cardiovascular disease. Int. J. Med. Inform. 145, 104326.
https://doi.org/10.1016/j.ijmedinf.2020.104326
Karunamurthy, A., Kulunthan, K., Dhivya, P., & Vickson, A. V. S. (2022). A Knowledge Discovery Based System Predicting Modelling for Heart Disease with Machine Learning. Quing: International Journal of Innovative Research in Science and Engineering, 1(1), 14-22.
https://doi.org/10.54368/qijirse.1.1.0005
Krishna Prasad, K., Aithal, P. S., Bappalige, N. N., & Soumya, S. (2021). An Integration of Cardiovascular Event Data and Machine Learning Models for Cardiac Arrest Predictions. International Journal of Health Sciences and Pharmacy (IJHSP), 5(1), 55-54.
https://doi.org/10.47992/IJHSP.2581.6411.0061
Mathur, P., Srivastava, S., Xu, X., & Mehta, J. L. (2020). Artificial intelligence, machine learning, and cardiovascular disease. Clinical Medicine Insights: Cardiology, 14, 1179546820927404.
https://doi.org/10.1177/1179546820927404
Mehmood, A., Iqbal, M., Mehmood, Z., Irtaza, A., Nawaz, M., Nazir, T., Masood, M. (2021). Prediction of heart disease using deep convolutional neural networks. Arab J Sci Eng. 46(4), 3409-3422.
https://doi.org/10.1007/s13369-020-05105-1
Miller, D. D. (2020). Machine intelligence in cardiovascular medicine. Cardiology in Review, 28(2), 53-64.
https://doi.org/10.1097/CRD.0000000000000294
Nandy, S., Adhikari, M., Balasubramanian, V., Menon, V.G., Li, X., Zakarya, M. (2023). An intelligent heart disease prediction system based on a swarm-artificial neural network. Neural. Comput. Appl. 35(20), 14723-14737.
https://doi.org/10.1007/s00521-021-06124-1
Rani, P., Kumar, R., Ahmed, N.M.S., Jain, A. (2021). A decision support system for heart disease prediction based upon machine learning. J. Reliab. Intell. Environ. 7(3), 263-275.
https://doi.org/10.1007/s40860-021-00133-6
Sabanci, K., Aslan, M.F., Ropelewska, E., Unlersen, M.F. (2022). A convolutional neural network-based comparative study for pepper seed classification: Analysis of selected deep features with support vector machine. J. Food Process Eng. 45(6), e13955.
https://doi.org/10.1111/jfpe.13955
Sinha, N., Kumar, M.G., Joshi, A.M., Cenkeramaddi, L.R. (2023). DASMcC: Data Augmented SMOTE Multi-class Classifier for prediction of Cardiovascular Diseases using time series features. IEEE Access.
https://doi.org/10.1109/ACCESS.2023.3325705
Townsend, N., Kazakiewicz, D., Lucy Wright, F., Timmis, A., Huculeci, R., Torbica, A., Vardas, P. (2022). Epidemiology of cardiovascular disease in Europe. Nat. Rev. Cardiol. 19(2), 133-143.
https://doi.org/10.1038/s41569-021-00607-3
Ullah, M., Hamayun, S., Wahab, A., Khan, S.U., Rehman, M.U., Haq, Z.U., Naeem, M. (2023). Smart technologies used as smart tools in the management of cardiovascular disease and their future perspective. Curr Probl Cardiol. 48(11), 101922.
https://doi.org/10.1016/j.cpcardiol.2023.101922
Wazery, Y.M., Saber, E., Houssein, E.H., Ali, A.A., Amer, E. (2021). An efficient slime mould algorithm combined with k-nearest neighbor for medical classification tasks. IEEE Access. 9, 113666-113682.
https://doi.org/10.1109/ACCESS.2021.3105485
Yang, Z., Wang, W., Huang, P., Gao, G., Wu, X., Zhang, F. (2021). Local median based linear regression classification for biometric recognition. Comput. Electr. Eng. 96, 107509.
https://doi.org/10.1016/j.compeleceng.2021.107509
View Dimensions
View Altmetric
Save
Citation
View
Share