Stroke Prediction Model Based on Decision Tree
In this paper, the predictive model of stroke based on decision tree is implemented to predict the stroke probability of ten samples by using Python language. The dataset of stroke is collected and is preprocessed, then the Gini coefficients of each feature are calculated to select the division, and...
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Published in | WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE Vol. 20; pp. 24 - 27 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
07.03.2023
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Online Access | Get full text |
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Abstract | In this paper, the predictive model of stroke based on decision tree is implemented to predict the stroke probability of ten samples by using Python language. The dataset of stroke is collected and is preprocessed, then the Gini coefficients of each feature are calculated to select the division, and then the decision tree model is obtained. Finally, the stroke probability is predicted for ten samples. In addition, Naive Bayes model is applied to predict the stroke probability to compare with the decision tree method. The experimental results show that older people with high blood pressure, heart disease, habitual smoking are more possible to have stroke, with a prediction accuracy of 88% for decision tree method and 79% for Naive Bayes model, respectively. |
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AbstractList | In this paper, the predictive model of stroke based on decision tree is implemented to predict the stroke probability of ten samples by using Python language. The dataset of stroke is collected and is preprocessed, then the Gini coefficients of each feature are calculated to select the division, and then the decision tree model is obtained. Finally, the stroke probability is predicted for ten samples. In addition, Naive Bayes model is applied to predict the stroke probability to compare with the decision tree method. The experimental results show that older people with high blood pressure, heart disease, habitual smoking are more possible to have stroke, with a prediction accuracy of 88% for decision tree method and 79% for Naive Bayes model, respectively. |
Author | Zheng, Xiaoyang Liu, Yuheng He, Jiangping Zhang, Chenxuan Liu, Yuhan |
Author_xml | – sequence: 1 givenname: Yuheng surname: Liu fullname: Liu, Yuheng organization: School of Artificial Intelligence, Liangjiang Chongqing University of Technology Chongqing, 401135, P.R.CHINA – sequence: 2 givenname: Chenxuan surname: Zhang fullname: Zhang, Chenxuan organization: School of Artificial Intelligence, Liangjiang Chongqing University of Technology Chongqing, 401135, P.R.CHINA – sequence: 3 givenname: Xiaoyang surname: Zheng fullname: Zheng, Xiaoyang organization: School of Artificial Intelligence, Liangjiang Chongqing University of Technology Chongqing, 401135, P.R.CHINA – sequence: 4 givenname: Yuhan surname: Liu fullname: Liu, Yuhan organization: School of Artificial Intelligence, Liangjiang Chongqing University of Technology Chongqing, 401135, P.R.CHINA – sequence: 5 givenname: Jiangping surname: He fullname: He, Jiangping organization: School of Artificial Intelligence, Liangjiang Chongqing University of Technology Chongqing, 401135, P.R.CHINA |
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