Predicting of Stroke Risk Based On Clinical Symptoms Using the Logistic Regression Method

Subject Areas : International Journal of Industrial Mathematics

مائده غلام آزاد ¹ , جعفر پورمحمود ² , علیرضا آتشی ³ , مهدی فرهودی ⁴ , رضا دلجوان انوری ⁵

1 - Department of Applied Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran.
2 - Department of Applied Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran.
3 - Department of E-Health, Virtual School, Tehran University of Medical Sciences, Tehran, Iran.
4 - Neurosciences Research Center, Tabriz university of medical sciences, Tabriz, Iran.
5 - Neurosciences Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.

Received: 2021-11-16 Accepted : 2022-02-09 Published : 2022-04-01

Keywords: ‎Prediction‎, ‎Logistic Regression‎, ‎Risk Factors‎, Classification, Stroke Risk,

Abstract :

Mathematical modeling is one of the feasible methods that can be used to solve real problems. Modeling can be done using a variety of methods, including statistical methods that can be used to predict a variety of events. Health is one of the most important areas of research in the world today. Among the various diseases in the health sector, this study concerns stroke which is the second leading cause of death and long-term human disability, that has led to doing this research. The main objective of this research is to design and to build a predictive model of stroke based on symptoms and clinical reports, whether or not stroke occurs in patients in the near future. Using logistic regression technology, the main pathogenic factors of stroke have been found and their incidence has been predicted. In this study, clinical information from 5411 patients was collected and, after applying the LR method, the predictive model was designed.

References:

 [1] J. Brownlee, Linear Regression for Machine Learning, Machine Learning Algorithms, (2020) http://machinelearningmastery.com.
[2] D. C. Charlesworth, D. S. Likosky, C. A. S. Marrin, Development and Validation of a Prediction Model for Strokes after Coronary Artery Bypass Grafting, The Society of Thoracic Surgeons Published by Elsevier Inc. 76 (2003) 436-443.
[3] K. L. Chien, T. C. Su, H. C. Hsu, Constructing the Prediction Model for the Risk of Stroke in a Chinese Population Report from a Cohort Study in Taiwan, Stroke journal 22 (2010) 1858-1864.
[4] W. Cole A Physico-Medical Essay Concerning the Late Frequency of Apoplexies Together With a General Method of Their Prevention and Cure: In a Letter to a Physician. Oxford, United Kingdom: The Theater. 1869. Reprinted by: New York, Classics of Neurology Neurosurgery Library (1995).
[5] M. A. Dronne, J. P. Boissel, E. Grenier, A mathematical model of ion movements in grey matter during a stroke, Journal of Theoretical Biology 240 (2006) 599-615.
[6] R. Deljavan, M. Farhoudi, H. SadeghiBazargani, Stroke in-hospital survival and its predictors: the first results from Tabriz Stroke Registry of Iran Internationa Journal of General Medicin 11 (2018) 233-240.
[7] H. Ebrahimi, S. Haghjoo-Javanmard, S. Asgary, Opium Addiction and Ischemic Stroke in Isfahan, Iran: A Case-Control Study, European neurology 79 (2018) 82-85.
[8] Y. Furuta, J. Hata, N. Mukai, Secular trends in the incidence, risk factors, and prognosis of transient ischemic attack in Japan: The Hisayama Study, Atherosclerosis 273 (2018) 84-90.
[9] M. Farhoudi, K. Mehrvar, H. SadeghiBazargani, Stroke subtypes, risk factors and mortality rate in the northwest of Iran, Iran J Neurol 163 (2017) 112-117.
[10] A. Huppert, G. Katriel , Mathematical modeling and prediction in infectious disease epidemiology, European Society of Clinical Microbiology and Infectious Diseases 19 (2013) 999-1005.
[11] S. H. Jee, J. W. Park, S. Y. Lee, Stroke risk prediction model: A risk profile from the Korean study, Atherosclerosis 197 (2008) 318-325.
[12] R. W. Mauthe, D. C. Haaf , P. Hayn, Predicting Discharge Destination of Stroke Patients Using a Mathematical Model Based on Six Items From the Functional Independence Measure, Academy of Physical Medicine and Rehabilitation 77 (1996) 100-103.
[13] S. McClean, Y. Qiao, K. Fullerton, STOPGAP: Stroke patient management and capacity planning, Operation research for health care 6 (2015) 78-86.
[14] S. Mathisen, L. Delan, J. Petter, Long- Term mortality and its risk factors in stroke survivors, Journal of stroke and cerebrovascular disease 25 (2016) 635-641.
[15] J. Markidan, J. W. Cole, C. A. Cronin, Smoking and Risk of Ischemic Stroke in Young Men, Stroke journal 49 (2018) 1276-1278.
[16] I. Ruczinski, C. Kooperbery, Logic Regression, Journal of Computational and Graphical Statistics 12 (2003) 475- 511.
[17] L. Torgo, J. Gama, Regression classification, Brazilian Symposium on Artificial Intelligence (1996).
[18] M. Talebi, M. Ghertasi, A. Taheraghdam, A comparison of risk factors and severity of ischemic stroke in female and male genders in North-West Iran: A cross-sectional study, Iranian Journal Neural 13 (2014) 215-219.
[19] B. Vahdani, Sh. Sadigh Behzadi, S. M. Mousavi, An artificial intelligence model based on LS-SVM for third-party logistics provider selection, International Journal of Industrial Mathematics 7 (2015) 301-311.
[20] World Health Organization, World health report 2000- health systems: improving performance, World Health Organization, Geneva, Switzerland (2000).
[21] Y. Zhuo, H. Yu, Zh. Yang, Prediction factors of recurrent stroke among Chinese adults using retional vasculature characteristics, Journal of stroke and cerebrovascular disease 26 (2017) 679-685.
[22] M. M. Zhaoxi, Y. MM. Yushan, L.MD. Yan, W. MM. Wanhua, C. MD. Yongjun, F. MD. Qi, Clinical Utility of the Inflammatory Factors Combined With Lipid Markers in the Diagnostic and Prognostic Assessment of Ischemic Stroke: Based on Logistic Regression Models, Journal of Stroke and Cerebrovascular Diseases 29 (2020) 104-121.