Discriminant analysis of pulmonary tuberculosis patients and pneumonia patients based on machine learning
10.19405/j.cnki.issn1000-1492.2025.03.017
- Author:
Minli Chang
1
;
Shuping You
2
;
Xiaodie Chen
1
;
Zhifei Chen
3
;
Yanling Zheng
3
Author Information
1. College of Public Health，Xinjiang Medical University，Urumqi 830017
2. College of Nursing，Xinjiang Medical University，Urumqi 830017
3. College of Medical Engineering and Technology，Xinjiang Medical University，Urumqi 830017
- Publication Type:Journal Article
- Keywords:
pulmonary tuberculosis;
pneumonia;
support vector machine;
random forest;
neural network model
- From:
Acta Universitatis Medicinalis Anhui
2025;60(3):507-514
- CountryChina
- Language:Chinese
-
Abstract:
Objective :To explore the feasibility of machine learning methods in the discrimination of tuberculosis patients.
Methods :The data of 15 observation indicators of 860 patients were obtained from a tertiary hospital. Through in-depth mining and analysis of the data, support vector machine, random forest and neural network model methods were used to discriminate the diseases of patients.
Results :The accuracies of the TB suspected patient discrimination models based on support vector machine, random forest and neural network were 90%, 91% and 88%, respectively.
Conclusion :All three machine learning methods can be used for discriminative analysis of suspected tuberculosis patients. In comparison, random forest performs better in discriminating patients with tuberculosis from those with pneumonia.
- Full text:2026012318243520113基于CD161构建肺结核的临床诊断模型_张莹.pdf
