The confusion Matrix for various ML models.

The Ministry of Health and Social Welfare of South Africa has made significant efforts to combat tuberculosis (TB), guided by the National Strategic Plan for addressing HIV, STIs, and TB. However, progress in preventing and eradicating TB has been seriously hindered by reliance on ineffective diagnostic methods. This study aimed to predict and improve TB diagnosis in South Africa using machine learning techniques. Data from the National Income Dynamics Survey, conducted by the Southern African Labour and Development Research Units, were analyzed. The dataset underwent a 70:30 train-test split for Random Forest (RF), Decision Trees (DTs), Support Vector Machines (SVMs), Gradient Boosting Machines (GBMs), Artificial Neural Networks (ANNs), and Logistic Regression (LR). Hyperparameter tuning and impurity-based measures were employed to rank variable importance. RF achieved 87.50% sensitivity and an F1-score of 92.5%. DT achieved a sensitivity of 90.92% and an F1-score of 93.01%. ANN yielded 81.72% sensitivity and an F1-score of 87.53%. SGBMs showed 91.32% sensitivity and 94.55% F1-score. SVMs showed 90.03% sensitivity and 97.72% F1-score. LR achieved a sensitivity of 96.55% and an F1-score of 96.80%. Machine Learning (ML) techniques, with accuracy rates of more than 80% present a significant opportunity for enhancing TB prediction and diagnosis in South Africa. This predictive technique may be beneficial in resource-constrained settings, including those in sub-Saharan Africa.南非卫生与社会福利部在应对HIV、性传播感染（STIs）与结核病（TB）的国家战略计划指引下，为结核病防控工作付出了诸多努力。然而，依赖低效诊断方法的现状严重阻碍了结核病预防与根除工作的推进。本研究旨在借助机器学习技术，对南非的结核病诊断进行预测并优化其流程。研究分析了由南非劳工与发展研究单元（Southern African Labour and Development Research Units）开展的全国收入动态调查（National Income Dynamics Survey）所获取的数据。随后将数据集以7:3的比例划分为训练集与测试集，分别用于随机森林（Random Forest, RF）、决策树（Decision Trees, DTs）、支持向量机（Support Vector Machines, SVMs）、梯度提升机（Gradient Boosting Machines, GBMs）、人工神经网络（Artificial Neural Networks, ANNs）以及逻辑回归（Logistic Regression, LR）模型的训练与测试。研究采用超参数调优与基于不纯度的指标对变量重要性进行排序。其中，随机森林模型的敏感度达87.50%，F1分数为92.5%；决策树模型敏感度为90.92%，F1分数为93.01%；人工神经网络的敏感度为81.72%，F1分数为87.53%；SGBMs敏感度达91.32%，F1分数为94.55%；支持向量机（SVMs）敏感度为90.03%，F1分数为97.72%；逻辑回归（LR）敏感度达96.55%，F1分数为96.80%。机器学习（Machine Learning, ML）技术的准确率均超80%，为提升南非结核病预测与诊断能力提供了重要契机。此类预测技术在包括撒哈拉以南非洲在内的资源受限场景中或将发挥积极作用。