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20 October 2022 Stroke probability prediction and data visualization based on multiple machine learning models
Xinyue Huang, Meng Qi, Yihan Wang, Juntian Yang
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Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124514P (2022) https://doi.org/10.1117/12.2656801
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China
Abstract
The issue of stroke happening in world range acting a significant problem for human health. Until 2019, there were about 411 death cases per day in America which is 3 years earlier than the appearance of e-cigarettes in 2022. However, the judgement between different doctors could be variable and a slightly human error in complex situations could lead to a bad estimation result. To deal with this, the prediction of stroke probability of patients could provide an objective reference for doctors. This study made a data treatment of factors choosing and translation then calculating 4 accuracy of models and finally an application. To be more specific, an online stroke dataset was employed for factor analysis firstly and three machine learning algorithms were utilized to select most important stroke-related factors. 6 most influential factors were further passed into four machine learning models for training. The work gave the result of the highest accuracy performance of Random Forest between the four models which could be assistance of the prediction in stroke.
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Xinyue Huang, Meng Qi, Yihan Wang, and Juntian Yang "Stroke probability prediction and data visualization based on multiple machine learning models", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124514P (20 October 2022); https://doi.org/10.1117/12.2656801
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KEYWORDS
Data modeling
Machine learning
Factor analysis
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