Applications of Decision Tree Models and Logistic Regression in the Prediction of Active Tuberculosis

Hao FAN; Xing LIU; Le ZHANG; Weixian LI; Xuejiao WU; Yi HAN; Xiaodie YAO

doi:10.12259/j.issn.2095-610X.S20230916

Volume 44 Issue 9

Sep. 2023

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Hao FAN, Xing LIU, Le ZHANG, Weixian LI, Xuejiao WU, Yi HAN, Xiaodie YAO. Applications of Decision Tree Models and Logistic Regression in the Prediction of Active Tuberculosis[J]. Journal of Kunming Medical University, 2023, 44(9): 25-31. doi: 10.12259/j.issn.2095-610X.S20230916

Citation:

Hao FAN, Xing LIU, Le ZHANG, Weixian LI, Xuejiao WU, Yi HAN, Xiaodie YAO. Applications of Decision Tree Models and Logistic Regression in the Prediction of Active Tuberculosis[J]. Journal of Kunming Medical University, 2023, 44(9): 25-31. doi: 10.12259/j.issn.2095-610X.S20230916

Citation:

Hao FAN, Xing LIU, Le ZHANG, Weixian LI, Xuejiao WU, Yi HAN, Xiaodie YAO. Applications of Decision Tree Models and Logistic Regression in the Prediction of Active Tuberculosis[J]. Journal of Kunming Medical University, 2023, 44(9): 25-31. doi: 10.12259/j.issn.2095-610X.S20230916

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Applications of Decision Tree Models and Logistic Regression in the Prediction of Active Tuberculosis

doi: 10.12259/j.issn.2095-610X.S20230916

1.
School of Pharmaceutical Sciences，Dali University，Dali Yunnan 671000
2.
Yunnan Clinical Center for Infectious Diseases/ Dept. of Pharmacy，The 3rd People's Hospital of Kunming，Kunming Yunnan 650041，China

Received Date: 2023-05-17
Available Online: 2023-09-21
Publish Date: 2023-09-30

Abstract

Abstract

Objective To analyze the influence factors of active tuberculosis（ATB） using logistic regression model and decision tree model, and to provide a reference point for ATB prevention and control. Methods The experimental group consisted of 200 active pulmonary tuberculosis patients admitted to the Third People’s Hospital of Kunming from March 2021 to March 2023, and the control group consisted of 200 healthy individuals who underwent physical examination during the same period Logistic regression. Decision tree ATB risk prediction models were established, and the decision tree analysis models（Decision Tree 1 and Decision Tree 2） were set up under the condition of whether they were based on the results of Logistic regression or not, and the decision tree analysis models（Decision Tree 1 and Decision Tree 2） were set up. The prediction effect of the three models was evaluated using the subjects' work curves. Results The results of Logistic regression showed that AAT, IL-4, IL-6, IL-17 and IFN-γ were the risk factors for ATB with CD+4 as the protective factor. The results of Decision Tree 1 showed that CRP was the root node, followed by IL-1, IL-6, CD⁺4, IL-17, AGP, and IFN-γ as the sub-nodes, respectively. Decision Tree 2 showed that IL-6 was the root node, followed by AAT, IL-4, IL-17 as sub-nodes. The established risk prediction models showed an AUC of 0.887 for logistic regression and 0.900（Decision Tree 1） and 0.857（Decision Tree 2） for the decision tree model. The AUC comparison results of the three models showed that the AUC of Decision Tree 1 was better than that of Decision Tree 2（95% CI: 0.0019-0.0841, P < 0.05）, but the difference with Logistic regression model was not statistically significant（95% CI: 0.0265-0.0522, P = 0.526）. Conclusion Both Logistic model and Decision Tree 1 model have certain application value in predicting the risk factors of ATB, and it is recommended to combine the two models so as to provide the better reference value for the laboratory prediction of ATB.
- Active tuberculosis,
- Risk factors,
- Logistic regression,
- Decision tree model

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References(21)

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