Author(s)

XinChun Wang

Affiliation(s)

School of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, Guangxi, China.

Corresponding Author

XinChun Wang

ABSTRACT

With the continuous development of big data, statistical model analysis has permeated various fields of life, particularly in clinical medicine. In addressing the clinical prediction of patients' stroke probability, modeling methods such as ANOVA, support vector machines, binomial logistic regression analysis and random forest models are essential. Given that the dependent variable in this study is a binary classification problem, logistic regression analysis and random forest models were selected for the analysis. This paper elaborates on the principles of logistic regression analysis and random forest models and random forest models, providing the regression equation for the regression model and the importance scores of each variable in the random forest model. Additionally, the predictive capabilities of these two models were evaluated, including an assessment of prediction accuracy.Through the application of regression and random forest models, we aim to enhance the clinical prediction accuracy of patients' stroke probability, thereby providing a more reliable basis for clinical decision-making.

KEYWORDS

Stroke; Influencing factors; Logistic Regression Analysis; Random forest regression model; Prediction accuracyt

CITE THIS PAPER

XinChun Wang. Analysis and prediction of influencing factors on the probability of stroke. Trends in Social Sciences and Humanities Research. 2024, 2(4): 73-77. DOI: 10.61784/tsshr3013.

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