PREDICTIVE MODELING OF STROKE OCCURRENCE USING PYTHON FOR IMPROVED RISK ASSESSMENT

Đorđe PUCAR; Vladimir ŠIMOVIĆ

doi:10.5937/jpmnt12-50921

Đorđe PUCAR Faculty of Applied Management, Economics and Finance, Belgrade, Belgrade, University Business Academy in Novi Sad, Serbia
Vladimir ŠIMOVIĆ Faculty of Economics and Engineering Management, Novi Sad, University Business Academy in Novi Sad, Serbia

DOI: https://doi.org/10.5937/jpmnt12-50921

Ključne reči: Python, ML, Binary Classification, Logistic Regression, Random Forests

Sažetak

This paper examines the use of Machine Learning (ML) techniques, particularly Logistic Regression and Random Forests, to predict the occurrence of strokes. It integrates demographic, clinical, and lifestyle factors. The study uses Python as the primary tool for model development and analysis, focusing on binary classification to categorize individuals as either having had a stroke or not. The dataset includes attributes such as age, gender, hypertension, smoking status, and more, which are used to train and evaluate the models. Through extensive experimentation and evaluation, the paper demonstrates the effectiveness of Logistic Regression and Random Forests in stroke prediction. Logistic Regression provides a straightforward baseline, while Random Forests offer higher predictive accuracy. The findings highlight the importance of ML-based approaches in healthcare risk assessment and showcase Python's versatility in facilitating such analyses.

Reference

Bonkhoff, A. K., & Grefkes, C. (2022). Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence. Brain, 145(2), 457-475.

Couronné, R., Probst, P., & Boulesteix, A. L. (2018). Random forest versus logistic regression: a large-scale benchmark experiment. BMC bioinformatics, 19, 1-14.

Fernandez-Lozano, C., Hervella, P., Mato-Abad, V., Rodríguez-Yáñez, M., Suárez-Garaboa, S., López-Dequidt, I., Estany-Gestal, A., Sobrino, T., Campos, F., Castillo, J., Rodríguez-Yáñez, S., & Iglesias-Rey, R. (2021). Random forest-based prediction of stroke outcome. Scientific reports, 11(1), 10071. https://doi.org/10.1038/s41598-021-89434-7

Hajipour, F., Jozani, M. J., & Moussavi, Z. (2020). A comparison of regularized Logistic Regression and Random Forest Machine Learning models for daytime diagnosis of obstructive sleep apnea. Medical & Biological Engineering & Computing, 58(10), 2517–2529. doi:10.1007/s11517-020-02206-9

https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset (15.03.2024.)

Jing, Y. (2022). Machine Learning Performance Analysis to Predict Stroke Based on Imbalanced Medical Dataset. In CAIBDA 2022; 2nd International Conference on Artificial Intelligence, Big Data and Algorithms (pp. 1-7). Nanjing, China.

Kokkotis, C., Moustakidis, S., Giarmatzis, G., Giannakou, E., Makri, E., Sakellari, P., ... & Aggelousis, N. (2022). Machine Learning Techniques for the Prediction of Functional Outcomes in the Rehabilitation of Post-Stroke Patients: A Scoping Review. BioMed, 3(1), 1-20. https://doi.org/10.3390/biomed3010001

Maier, O., & Handels, H. (2016). Predicting Stroke Lesion and Clinical Outcome with Random Forests. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 219–230). Springer International Publishing. https://doi.org/10.1007/978-3-319-55524-9_21

Mainali, S., Darsie, M. E., & Smetana, K. S. (2021). Machine learning in action: stroke diagnosis and outcome prediction. Frontiers in neurology, 12, 734345. https://doi.org/10.3389/fneur.2021.734345

Merdas, H. M. (2024). Elastic Net–MLP–SMOTE (EMS)-Based Model for Enhancing Stroke Prediction. Medinformatics, 1(2), 73-78.

Poorani, K., Karuppasamy, M., Jansi Rani, M., & Prabha, M. (2023). Classifier Comparison for Stroke Prediction Ensembling SMOTE+ENN using Machine Learning Approach. Research Square. https://doi.org/10.21203/rs.3.rs-1675863/v1

Su, P. Y., Wei, Y. C., Luo, H., Liu, C. H., Huang, W. Y., Chen, K. F., ... & Lee, T. H. (2022). Machine learning models for predicting influential factors of early outcomes in acute ischemic stroke: registry-based study. JMIR Medical Informatics, 10(3), e32508. https://doi.org/10.2196/32508

Wang, W., Kiik, M., Peek, N., Curcin, V., Marshall, I. J., Rudd, A. G., ... & Bray, B. (2020). A systematic review of machine learning models for predicting outcomes of stroke with structured data. PloS one, 15(6), e0234722. https://doi.org/10.1371/journal.pone.0234722

Wu, Y., & Fang, Y. (2020). Stroke prediction with machine learning methods among older Chinese. International journal of environmental research and public health, 17(6), 1828. https://doi.org/10.3390/ijerph17061828

Zu, W., Huang, X., Xu, T., Du, L., Wang, Y., Wang, L., & Nie, W. (2023). Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review. Plos one, 18(6), e0287308. https://doi.org/10.1371/journal.pone.0287308