Abstract
With the development of science and technology, the application of data mining in medical field is becoming more and more popular. Machine learning methods also plays an important role in disease prediction. Stroke is characterized by high incidence rate, high disability rate, high mortality rate and high recurrence rate, and it is also likely to cause other kinds of complications. In this paper, each feature in the stroke dataset was analyzed in order to find out the factors affecting stroke and conducts classification and prediction research on whether there is a disease risk. Specifically, the PCA (Principal Component Analysis) algorithm is used to extract the main feature components of data, the SMOTE (Synthetic Minority Oversampling Technique) algorithm is used to adjust imbalanced feature categories. Traditional machine learning classification algorithms, such as decision tree, SVM(support vector machines), and various ensemble learning algorithms are used for the prediction of stroke risk, so as to study the relationship between stroke disease and each feature, and the classification prediction model, so that we can prevent strokes in time and reduce the risk of stroke. Among all the models, Bagging (Bootstrap aggregating) has the best performance with an ROC value of 0.97.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lou, J., Chen, Q., Liu, C., Xiao, L., Yang, H.: Visualization and analysis of research hotspots in stroke extended care. J. Nurse Repair 38(3), 209–215 (2023)
Wang, L., Peng, B., Zhang, H., Hongqi et al.: Summary of the china stroke prevention and control report 2020. Chin. J. Cerebrovasc. Dis. 19(2), 136–144 (2022)
Li, J., Zhang, Y., Li, H., Yang, Y.: Research progress of machine learning in stroke prediction. Henan Med. Res. 31(20), 3832–3835 (2022)
Gao, X., Yan, H., Lin, Q.: High risk factors and prediction of recurrent readmission in elderly ischemic stroke patients. Chin. J. Gerontol. 42(20), 5139–5141 (2022)
Zhang, Z., Hou, R.: Construction of a neural network-based prediction model for 180 d recurrence risk of stroke. Inf. Comput. (Theoretical Edition) 34(12), 14–16 (2022)
Yang, X., Zhou, X., Yin, X., et al.: Construction of a prognostic prediction model after mechanical thrombolysis for acute stroke based on NIHSS score and multimodal MRI. Chin. J. CT MRI 21(02), 14–16 (2023)
Ye, W., Tao, Y., Chen, X., et al.: A deeply integrated optimization method for multicategorical prognosis prediction of stroke. Comput. Eng. Appl. 59(05), 95–105 (2023)
Lin, P., et al.: A transferable deep learning prognosis model for predicting stroke patients’ recovery in different rehabilitation trainings. IEEE J. Biomed. Health Inform. 26(12), 6003–6011 (2022)
Zhang, X., Zhang, L., Sui, R.: Construction of a debility prediction model for elderly stroke patients based on logistic regression and artificial neural network. Mil. Nurs. 40(02), 10–14+19 (2023)
Zhou, W., Zhu, M., Lu, Y., Cheng, M., Li, X.: Estimating stroke risk in an elderly population using the framingham stroke probability model. Shanghai Prev. Med. 27(10), 598–601 (2015)
Ling, C.X., Huang, J., Zhang, H.: AUC: a better measure than accuracy in comparing learning algorithms. In: Xiang, Y., Chaib-draa, B. (eds.) Advances in Artificial Intelligence. Canadian AI 2003. LNCS, vol. 2671, pp. 329–341. Springer, Berlin (2003). https://doi.org/10.1007/3-540-44886-1_25
Acknowledgements
This work was sponsored by Shanghai Municipal Education Commission under the contract Z90004.23.001 (Professional Master’s Degree Authorized School Training Project). And this work was also partially sponsored by Sanda University under the contract A020201.23.058 (Key Courses Construction Project).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zuo, X., Guo, X., Yin, Z., Tseng, SP. (2024). Prediction and Analysis of Stroke Risk Based on Ensemble Learning. In: Pan, JS., Pan, Z., Hu, P., Lin, J.CW. (eds) Genetic and Evolutionary Computing. ICGEC 2023. Lecture Notes in Electrical Engineering, vol 1114. Springer, Singapore. https://doi.org/10.1007/978-981-99-9412-0_32
Download citation
DOI: https://doi.org/10.1007/978-981-99-9412-0_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9411-3
Online ISBN: 978-981-99-9412-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)