LOGISTIC REGRESSION TECHNIQUE FOR CARDIOVASCULAR DISEASE PREDICTION
Abstract
Cardiovascular diseases (CVDs) are the most common cause of death in the world. Over four out of five CVD deaths are due to heart attacks and strokes. CVD high mortality has led to about 17 million deaths worldwide. Several machine and deep learning techniques are used to classify the presence and absence of CVD. This paper presents a logistic regression (LR) technique for predicting the risk of heart diseases (HD). The goal is to create an LR algorithm and build a prediction model that would foretell the development of HD. The dataset included data on 207 patients, featuring the following: age, sex, chest pain type, blood pressure, cholesterol levels, fasting blood sugar > 120 mg/dl, electrocardiogram results, maximum heart rate, exercise-induced angina, ST depression, slope of the ST segment, number of major vessels colored by fluoroscopy, and thallium scan results. Using this dataset to train the LR technique, a robust model was created to accurately predict the existence of HD in new patients. With an accuracy of 81%, a precision of 83%, and a recall score of 76%, the accuracy, precision, and recall key metrics were used to evaluate the model's efficacy. The model’s accuracy was compared to alternative methods, such as K-Nearest Neighbors and Decision Tree classifiers, which yielded accuracy of 81% and 76%, respectively. The obtained results are of great significance for healthcare providers – the proposed model can assist in identifying those who are at high risk of heart diseases and allow for early implementation of prophylactic...
References
Ali, F., El-Sappagh, S. H. A., Islam, S. M. R., Kwak, D., Ali, A., Imran, M., & Kwak, K. S., 2020. A Smart Healthcare Monitoring System for Heart Disease Prediction Based on Ensemble Deep Learning and Feature Fusion. Information Fusion, 63, 208–222. https://doi.org/10.1016/j.inffus.2020.06.008 DOI: https://doi.org/10.1016/j.inffus.2020.06.008
Alim, M.A., Habib, S., Farooq, Y. & Rafay, A., 2020. Robust Heart Disease Prediction: A Novel Approach Based on Significant Feature ¸and Ensemble Learning Model, In 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 1–5. IEEE. DOI: https://doi.org/10.1109/iCoMET48670.2020.9074135
Anggoro, D. A., & Aziz, N. C., 2021. Implementation of K-Nearest Neighbors Algorithm for Predicting Heart Disease Using Python Flask. Iraqi Journal of Science, 62(9), 3196–3219. https://doi.org/10.24996/ijs.2021.62.9.33 DOI: https://doi.org/10.24996/ijs.2021.62.9.33
Bashir, S. A., Luka S., & Ibrahim M. (2023). Cardiovascular disease prediction using random forest machine learning algorithm. Fudma Journal of Sciences, 7(6), 282-289. https://doi.org/10.33003/fjs-2023-0706-2128 DOI: https://doi.org/10.33003/fjs-2023-0706-2128
Bind, S. C., & Pradhan, P. K., 2020. Heart Disease Prediction Using Machine Learning. International Research Journal of Engineering and Technology (IRJET), 7(4), 2395–0056.
Boateng, E. Y. & Abaye, D. A, 2019. A review of the logistic regression model with emphasis on medical research. Journal of Data Analysis and Information Processing, 7, 190-207. https://doi.org/10.4236/jdaip.2019.74012 DOI: https://doi.org/10.4236/jdaip.2019.74012
Cleveland. UC Irvine Machine Learning Repository, https://archive.ics.uci.edu/. Accessed on March 14, 2024
Dash, S., Shakyawar, S.K., Sharma, M., & Kaushik, S., 2019. Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(54). https://doi.org/10.1186/s40537-019-0217-0 DOI: https://doi.org/10.1186/s40537-019-0217-0
García-Ordás, M.T., Bayón-Gutiérrez, M., Benavides, Aveleira-Mata, J., & Benítez-Andrades, J. A, 2023. Heart disease risk prediction using deep learning techniques with feature augmentation. Multimedia Tools and Applications, 82, 31759–31773. https://doi.org/10.1007/s11042-023-14817-z DOI: https://doi.org/10.1007/s11042-023-14817-z
Harshitha, V. S., & Barlapudi, S., 2023. Predictions of Diabetic Mellitus using ML Techniques: A Systematic Overview. 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), 43-47. IEEE.
Kanade, V., 2022. What Is Logistic Regression? Equation, Assumptions, Types, and Best Practices. Artificial Intelligence, 2022. https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-logistic-regression/ , accessed on March 14, 2024
Liew, B.X.W., Kovacs, F.M., Rügamer, D. & Royuela, A., 2022. Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain. European Spine Journal, 31, 2082–2091. https://doi.org/10.1007/s00586-022-07188-w DOI: https://doi.org/10.1007/s00586-022-07188-w
Leukel, J., Özbek, G. & Sugumaran, V, 2024. Application of logistic regression to explain internet use among older adults: a review of the empirical literature. Universal Access in the Information Society, 23, 621–635. https://doi.org/10.1007/s10209-022-00960-1 DOI: https://doi.org/10.1007/s10209-022-00960-1
Matheson, M. B., Kato, Y., Baba, S., Cox, C., Lima, J. A. C., & Ambale-Venkatesh, B., 2022. Cardiovascular Risk Prediction Using Machine Learning in a Large Japanese Cohort. Circulation reports, 4(12), 595–603. https://doi.org/10.1253/circrep.CR-22-0101 DOI: https://doi.org/10.1253/circrep.CR-22-0101
Mc Namara, K., Alzubaidi, H., & Jackson, J. K., 2019. Cardiovascular disease as a leading cause of death: how are pharmacists getting involved? Integrated Pharmacy Research & Practice, 8: 1–11. https://doi.org/10.2147/IPRP.S133088 DOI: https://doi.org/10.2147/IPRP.S133088
Ogunpola, A., Saeed, F., Basurra, S., Albarrak, A. M., Qasem, S. N., 2024. Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases. Diagnostics, 14, 144. https://doi.org/10.3390/diagnostics14020144 DOI: https://doi.org/10.3390/diagnostics14020144
Panda, N. R., Kumar, P. J., Mohanty, J. N., Bhuyan, R.,2022. A Review on Logistic Regression in Medical Research. National Journal of Community Medicine, 13 (4), 265-270. https://doi.org/10.55489/njcm.134202222 DOI: https://doi.org/10.55489/njcm.134202222
Parekh, A. E., Shaikh, O. A., Simran, Manan, S., & Hasibuzzaman, M. A., 2023. Artificial intelligence (AI) in personalized medicine: AI-generated personalized therapy regimens based on genetic and medical history: short communication. Annals of medicine and surgery, 85(11), 5831–5833. https://doi.org/10.1097/MS9.0000000000001320 DOI: https://doi.org/10.1097/MS9.0000000000001320
Copyright (c) 2024 FUDMA JOURNAL OF SCIENCES
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.
FUDMA Journal of Sciences