Ghermi Mohamed, Messedi Meriam, Adida Chahira, Belarbi Kada, Djazouli Mohamed El Amine, Berrazeg Zahia Ibtissem, Kallel Sellami Maryam, Ghezini Younes, Louati Mahdi
Biology of Microorganisms and Biotechnology Laboratory, University of Oran1 Ahmed Ben Bella, Oran, Algeria.
Biotechnology Department, University of Oran1 Ahmed Ben Bella, Oran, Algeria.
Digit Health. 2024 Aug 30;10:20552076241278211. doi: 10.1177/20552076241278211. eCollection 2024 Jan-Dec.
Tuberculosis remains a major global health challenge, with delayed diagnosis contributing to increased transmission and disease burden. While microbiological tests are the gold standard for confirming active tuberculosis, many cases lack microbiological evidence, necessitating additional clinical and laboratory data for diagnosis. The complete blood count (CBC), an inexpensive and widely available test, could provide a valuable tool for tuberculosis diagnosis by analyzing disturbances in blood parameters. This study aimed to develop and evaluate a machine learning (ML)-based web application, TubIAgnosis, for diagnosing active tuberculosis using CBC data.
We conducted a retrospective case-control study using data from 449 tuberculosis patients and 1200 healthy controls in Oran, Algeria, from January 2016 to April 2023. Eight ML algorithms were trained on 18 CBC parameters and demographic data. Model performance was evaluated using balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC).
The best-performing model, Extreme Gradient Boosting (XGB), achieved a balanced accuracy of 83.3%, AUC of 89.4%, sensitivity of 83.3%, and specificity of 83.3% on the testing dataset. Platelet-to-lymphocyte ratio was the most influential parameter in this ML predictive model. The best performing model (XGB) was made available online as a web application called TubIAgnosis, which is available free of charge at https://yh5f0z-ghermi-mohamed.shinyapps.io/TubIAgnosis/.
TubIAgnosis, a ML-based web application utilizing CBC data, demonstrated promising performance for diagnosing active tuberculosis. This accessible and cost-effective tool could complement existing diagnostic methods, particularly in resource-limited settings. Prospective studies are warranted to further validate and refine this approach.
结核病仍然是一项重大的全球卫生挑战，诊断延迟导致传播增加和疾病负担加重。虽然微生物学检测是确诊活动性结核病的金标准，但许多病例缺乏微生物学证据，因此需要额外的临床和实验室数据来进行诊断。全血细胞计数（CBC）是一种廉价且广泛可用的检测方法，通过分析血液参数的紊乱情况，可为结核病诊断提供有价值的工具。本研究旨在开发并评估一种基于机器学习（ML）的网络应用程序TubIAgnosis，用于利用CBC数据诊断活动性结核病。
我们进行了一项回顾性病例对照研究，使用了2016年1月至2023年4月阿尔及利亚奥兰的449例结核病患者和1200例健康对照的数据。在18个CBC参数和人口统计学数据上训练了8种ML算法。使用平衡准确率、灵敏度、特异度、阳性预测值、阴性预测值和受试者工作特征曲线下面积（AUC）评估模型性能。
表现最佳的模型，即极端梯度提升（XGB）模型，在测试数据集上的平衡准确率为83.3%，AUC为89.4%，灵敏度为83.3%，特异度为83.3%。血小板与淋巴细胞比值是该ML预测模型中最具影响力的参数。表现最佳的模型（XGB）作为名为TubIAgnosis的网络应用程序在线提供，可在https://yh5f0z-ghermi-mohamed.shinyapps.io/TubIAgnosis/免费获取。
TubIAgnosis是一种利用CBC数据的基于ML的网络应用程序，在诊断活动性结核病方面表现出了有前景的性能。这种易于使用且具有成本效益的工具可以补充现有的诊断方法，特别是在资源有限的环境中。有必要进行前瞻性研究以进一步验证和完善这种方法。
