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)

References

[1]	Archer M C,McCollum J,Press C,et al. Stressed stability and protective efficacy of lead lyophilized formulations of ID93+GLA-SE tuberculosis vaccine[J]. Heliyon,2023,9(6):e17325. doi: 10.1016/j.heliyon.2023.e17325
[2]	宋敏,陆普选,方伟军,等. 2022年WHO全球结核病报告: 全球与中国关键数据分析[J]. 新发传染病电子杂志,2023,8(1):87-92.
[3]	Li K,Liu S X,Yang C Y,et al. A routine blood test-associated predictive model and application for tuberculosis diagnosis: A retrospective cohort study from northwest China[J]. J Int Med Res,2019,47(7):2993-3007. doi: 10.1177/0300060519851673
[4]	Small P M,Pai M. Tuberculosis diagnosis-time for a game change[J]. N Engl J Med,2010,363(11):1070-1071. doi: 10.1056/NEJMe1008496
[5]	Pai M,Kalantri S,Dheda K. New tools and emerging technologies for the diagnosis of tuberculosis: Part II. Active tuberculosis and drug resistance[J]. Expert Rev Mol Diagn,2006,6(3):423-432. doi: 10.1586/14737159.6.3.423
[6]	陈玉芊,王世军,王欣,等. IL-6、IL-8、IL-18、VEGF诊断活动性肺结核的价值[J]. 检验医学与临床,2023,20(2):224-227.
[7]	汪永强,刘世军,李显勇,等. 外周血Th17/CD4+CD+25+CD127lowTreg细胞区分活动性肺结核和潜伏期结核合并肺炎[J]. 中国实验诊断学,2022,26(11):1648-1655.
[8]	中华人民共和国国家卫生和计划生育委员会. 肺结核诊断标准(WS 288-2017)[J]. 新发传染病电子杂志,2018,3(1):59-61.
[9]	邓国防,路希维. 肺结核活动性判断规范及临床应用专家共识[J]. 中国防痨杂志,2020,42(4):301-307.
[10]	韩婷婷,刘桂珍,陈秋奇,等. 世界卫生组织《应对结核病及其共病合作行动框架》解读[J]. 中国防痨杂志,2023,45(1):25-30.
[11]	Feng Y,Wang J,Shao Z,et al. Predicting related factors of immunological response to hepatitis B vaccine in hemodialysis patients based on integration of decision tree classification and logistic regression[J]. Hum Vaccin Immunother,2021,17(9):3214-3220. doi: 10.1080/21645515.2021.1895603
[12]	李良俊,翟荣,邬闻文,等. Logistic回归及决策树模型在CCU老年病人睡眠障碍影响因素分析中的应用[J]. 护理研究,2022,36(16):2874-2879.
[13]	严建新,黄林瑶,江天. C-反应蛋白/白蛋白比值、单核细胞/淋巴细胞比值在肺结核患者中的应用价值[J]. 中国卫生检验杂志,2022,32(16):2016-2019.
[14]	Rohini K,Surekha Bhat M,Srikumar P S,et al. Assessment of hematological parameters in pulmonary tuberculosis patients[J]. Indian J Clin Biochem,2016,31(3):332-335. doi: 10.1007/s12291-015-0535-8
[15]	宋丹,熊晓蕃,杨雨,等. 巨噬细胞极性重塑在疾病和组织稳态中的作用[J]. 中国细胞生物学学报,2022,44(5):904-923.
[16]	龚文平,米洁,吴雪琼. 免疫活性物质: 结核病和非结核分枝杆菌病治疗的新选择[J]. 中国防痨杂志,2022,44(11):1107-1121.
[17]	Tesfa L,Koch F W,Pankow W,et al. Confirmation of Mycobacterium tuberculosis infection by flow cytometry after ex vivo incubation of peripheral blood T cells with an ESAT-6-derived peptide pool[J]. Cytometry B Clin Cytom,2004,60(1):47-53.
[18]	卢艳辉,刘振奎,李世阳,等. 血清学指标联合小儿危重病例评分和Brighton儿童早期预警评分预测脓毒症患儿死亡的巢式病例对照研究[J]. 中国全科医学,2019,22(15):1800-1806.
[19]	帅健,李丽萍,陈亚群,等. 决策树模型及Logistic回归模型在伤害发生影响因素分析中的作用[J]. 中华疾病控制杂志,2015,19(2):185-189.
[20]	邬闻文,谭晓东,孙东晗,等. Logistic回归分析模型和决策树分析在高血压糖尿病共患病危险因素中的应用[J]. 中华疾病控制杂志,2022,26(7):827-833.
[21]	李静,侯云霞,强万敏. 癌症患者非计划性再入院风险预测模型的范围综述[J]. 中华护理杂志,2022,57(9):1079-1087.

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