Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis

• Published in: PloS one 2024-10, Vol.19 (10), p.e0309151
• Main Authors: Ahamed Fayaz, Shaik, Babu, Lakshmanan, Paridayal, Loganathan, Vasantha, Mahalingam, Paramasivam, Palaniyandi, Sundarakumar, Karuppasamy, Ponnuraja, Chinnaiyan
• Format: Article
• Language: English
• Subjects: Accuracy; Adult; Algorithms; Analysis; Antitubercular Agents - therapeutic use; Artificial intelligence; Biology and Life Sciences; Care and treatment; Classification; Clinical trials; Computer and Information Sciences; Culture; Data analysis; Datasets; Decision trees; Diagnosis; Female; Health aspects; Health services; Humans; Learning algorithms; Machine Learning; Male; Medical research; Medicine and Health Sciences; Methods; Middle Aged; Mycobacterium tuberculosis - isolation & purification; Patients; Performance evaluation; Performance prediction; Physical Sciences; Prevalence studies (Epidemiology); Pulmonary tuberculosis; Research and Analysis Methods; Retrospective Studies; Software; Sputum; Sputum - microbiology; Statistical analysis; Statistical models; Success; Support Vector Machine; Support vector machines; Treatment Outcome; Tuberculosis; Tuberculosis, Pulmonary - diagnosis; Tuberculosis, Pulmonary - drug therapy; Tuberculosis, Pulmonary - microbiology
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0309151

Despite advancements in detection and treatment, tuberculosis (TB), an infectious illness caused by the Mycobacterium TB bacteria, continues to pose a serious threat to world health. The TB diagnosis phase includes a patient's medical history, physical examination, chest X-rays, and laboratory procedures, such as molecular testing and sputum culture. In artificial intelligence (AI), machine learning (ML) is an advanced study of statistical algorithms that can learn from historical data and generalize the results to unseen data. There are not many studies done on the ML algorithm that enables the prediction of treatment success for patients with pulmonary TB (PTB). The objective of this study is to identify an effective and predictive ML algorithm to evaluate the detection of treatment success in PTB patients and to compare the predictive performance of the ML models. In this retrospective study, a total of 1236 PTB patients who were given treatment under a randomized controlled clinical trial at the ICMR-National Institute for Research in Tuberculosis, Chennai, India were considered for data analysis. The multiple ML models were developed and tested to identify the best algorithm to predict the sputum culture conversion of TB patients during the treatment period. In this study, decision tree (DT), random forest (RF), support vector machine (SVM) and naïve bayes (NB) models were validated with high performance by achieving an area under the curve (AUC) of receiver operating characteristic (ROC) greater than 80%. The salient finding of the study is that the DT model was produced as a better algorithm with the highest accuracy (92.72%), an AUC (0.909), precision (95.90%), recall (95.60%) and F1-score (95.75%) among the ML models. This methodology may be used to study the precise ML model classification for predicting the treatment success of TB patients during the treatment period.