Original Article

Using Stacking methods based Genetic Algorithm to predict the time between symptom onset and hospital arrival in stroke patients and its related factors


Introduction: Early arrival of patients with acute ischemic stroke to start of treatment by recombinant tissue plasminogen activator (rt-PA) within 4.5 hours after onset of stroke. We aimed to develop a machine learning method to predict effective factors on arrival time of patients with stroke to hospital after symptom onset. Methods: We included 676 patients with ischemic stroke who referred to Ardabil city hospital a province in northwest of Iran at year 2018. Classification models such as Random forest (RF), Gradient Boosting Classifier (GB), Decision Tree Classifier (DT), Support-Vector Machines (SVM), Naïve Bayes (NB), artificial neural networks (ANN), and logistic regression (LR) with 10-fold cross-validation were developed to predict effective factors on arrival time of patient with stroke to hospital. The performances were evaluated with accuracy, sensitivity, specificity, positive prophetical worth, and negative prophetical worth.  Results: Of all patients, 25.3% arrived to the hospital in less than 4.5 hours. The accuracy of RF, NB, ANN, GB, DT, SVM, LR and suggest method (Stacking) were 0.98, 0.72, 0.73, 0.79, 0.98, 0.73, 0.74, and 0.99. Conclusion: In this study, the Stacking technique provide a better result (Accuracy 99.51%, sensitivity 100%, and specificity 99.40%) among all other techniques and this model could be used as a valuable tool for clinical decision making.

1. Saver JL, Fonarow GC, Smith EE, et al. Time to Treatment with Intravenous Tissue Plasminogen Activator and Outcome From Acute Ischemic Stroke. JAMA. 2013;309(23):2480–2488. doi:10.1001/jama.2013.6959.
2. Weintraub MI. Thrombolysis (tissue plasminogen activator) in stroke a medicollegal quagmire. Stroke. 2006; 37: 1917-22.
3. P. Groves, B. Kayyali, D. Knott, S.V. Kuiken, The big data revolution in healthcare: Accelerating value and innovation, 2016.
4. Marler JR, Tilley BC, Lu M, et al. Early stroke treatment associated with better outcome: the NINDS rt-PA study. Neurology. 2000; 55: 1649–55.
5. Stolz E, Hamann GF, Kaps M, et al. Regional differences in acute stroke admission and thrombolysis rates in the German federal State of Hesse. Dtsch Arztebl Int. 2011; 108(36): 607–9.
6. M Al Khathaami A, O.Mohammad Y, S.Alibrahim F and A.Jradi H. Factors associated with late arrival of acute stroke patients to emergency department in Saudi Arabia. SAGE Open Medicine. 2018;6:1-7.
7. Ashraf VV, Maneesh M, Praveenkumar R, Saifudheen K, Girija AS. Factors delaying hospital arrival of patients with acute stroke. Ann Indian Acad Neurol. 2015; 18(2): 162–6.
8. Chen CH, Huang P, Yang YH, Liu CK, Lin TJ, Lin RT. Pre-hospital and in-hospital delays after onset of acute ischemic stroke: a hospital-based study in southern Taiwan. Kaohsiung J Med Sci. 2007; 23(11): 552-9.
9. Hong ES, Kim SH, Kim WY, Ahn R, Hong JS. Factors associated with prehospital delay in acute stroke. Emerg Med J. 2011; 28(9): 790–3.
10. Moser DK, Kimble LP, Alberts MJ, Alonzo A, Croft JB, Dracup K, Evenson KR, Go AS, Hand MM, Kothari RU, Mensah GA, Morris DL, Pancioli AM, Riegel B, Zerwic JJ: Reducing delay in seeking treatment by patients with acute coronary syndrome and stroke: A scientific statement from the American Heart Association Council on cardiovascular nursing and stroke council. Circulation. 2006; 114: 168-182.
11. Kleindorfer DO, Lindsell CJ, Broderick JP, Flaherty ML, Woo D, Ewing I, Schmit P, Moomaw C, Alwell K, Pancioli A, Jauch E, Khoury J, Miller R, Schnider A, Kissela BM: Community socioeconomic status and prehospital times in acute stroke and transient ischemic attack. Do poorer patients have longer delays from 911 call to the emergency department?. Stroke. 2006; 37: 1508-1513.
12. Rajajee V, Saver J: Prehospital care of the acute stroke patient. Tech Vasc Interv Radiol. 2005; 8: 74-80.
13. Abdollahi, J., Moghaddam, B. N., & Parvar, M. E. (2019). Improving diabetes diagnosis in smart health using genetic-based Ensemble learning algorithm. Approach to IoT Infrastructure. Future Gen Distrib Systems J, 1, 23-30.
14. Abdollahi, J., Keshandehghan, A., Gardaneh, M., Panahi, Y., & Gardaneh, M. (2020). Accurate detection of breast cancer metastasis using a hybrid model of artificial intelligence algorithm. Archives of Breast Cancer, 18-24.
15. Abdollahi, J., & Nouri-Moghaddam, B. (2021). Hybrid stacked ensemble combined with genetic algorithms for Prediction of Diabetes. arXiv preprint arXiv:2103.08186.
16. Abdollahi, J., Nouri-Moghaddam, B., & Ghazanfari, M. (2021). Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases). arXiv preprint arXiv:2103.08182.
17. Abdollahi, J., & Nouri-Moghaddam, B. (2021). Feature selection for medical diagnosis: Evaluation for using a hybrid Stacked-Genetic approach in the diagnosis of heart disease. arXiv preprint arXiv:2103.08175.
18. Chiesa, M., Maioli, G., Colombo, G. I., & Piacentini, L. (2020). GARS: Genetic Algorithm for the identification of a Robust Subset of features in high-dimensional datasets. BMC bioinformatics, 21(1), 54.
19. Wu C.C, Yeh WC, Hsu WD, Islam M, Nguyen PA, Poly TA, ET AL. Prediction of fatty liver disease using machine learning algorithms. Computer Methods and Programs in Biomedicine 2019;170: 23–9.
20. T. Condie, P. Mineiro, N. Polyzotis, M. Weimer, Machine learning on big data. Data Engineering (ICDE), 2013 IEEE 29th International Conference on. IEEE (2013), 1242-1244.
21. T.B. Murdoch, A.S. Detsky, The inevitable application of big data to health care, Jama 309 (2013), 1351- 1352.
22. Sharma, A. K., Nandal, A., Dhaka, A., & Dixit, R. (2020). A survey on machine learning based brain retrieval algorithms in medical image analysis. Health and Technology, 1-15.
23. Açıcı, K., Sümer, E., & Beyaz, S. (2021). Comparison of different machine learning approaches to detect femoral neck fractures in x-ray images. Health and Technology, 1-11.
IssueVol 8 No 1 (2022) QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/jbe.v8i1.10401
Stroke Machine learning Classification Random Forest Hospital

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Amani F, Abdollahi J, mohammadnia alireza, amani paniz, fattahzadeh-ardalani ghasem. Using Stacking methods based Genetic Algorithm to predict the time between symptom onset and hospital arrival in stroke patients and its related factors. JBE. 2022;8(1):8-23.