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  4. Vol 5, Issue 2, 2024

Research on the Influencing Factors of Brain Stroke Based on Binary Logistic Regression and Random Forest

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DOI: 10.23977/medsc.2024.050206 | Downloads: 5 | Views: 60

Author(s)

Qiuyue Lin 1

Affiliation(s)

1 School of Science, Jimei University, Xiamen, 361000, China

Corresponding Author

Qiuyue Lin

ABSTRACT

Stroke is characterized by high incidence, recurrence, disability, mortality and economic burden, which is also a serious threat to people's lives and health and quality of life. This study analyzes data from the Kaggle website to develop a risk assessment model to explore the risk factors affecting the occurrence of stroke. The dataset includes 4981 cases and 10 variables such as gender, age and so on. The target variable is whether the respondent has had a brain stroke or not. In this study, at first, the accuracy of the training set is about 100%, and then the fitted model is tested, and the accuracy rate is still in a good state at 99%. Therefore, the assessment results of this random forest model are acceptable. To better assess the risk factors that trigger stroke, more comprehensive data and better sampling methods are needed. This model provides an important ranking of risk factors for stroke occurrence and provides a reference for stroke prevention.

KEYWORDS

Brain stroke, logistic regression, random forest

CITE THIS PAPER

Qiuyue Lin, Research on the Influencing Factors of Brain Stroke Based on Binary Logistic Regression and Random Forest. MEDS Clinical Medicine (2024) Vol. 5: 40-47. DOI: http://dx.doi.org/10.23977/medsc.2024.050206.

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