Artificial Neural Networks for Prediction of Tuberculosis Disease - Khan, Muhammad Tahir; Kaushik, Aman Chandra; Ji, Linxiang; Malik, Shaukat Iqbal; Ali, Sajid; Wei, Dong-Qing (Frontiers in Microbiology, 2019)
Background: The global burden of tuberculosis (TB) and antibiotic resistance is attracting the attention of researchers to develop some novel and rapid diagnostic tools. Although, the conventional methods like culture are considered as the gold standard, they are time consuming and offer more time in the transmission of disease. Further, the Xpert MTB/RIF assay offers the fast diagnostic facility within two hours, but due to low the sensitivity in some sample types may lead to more serious state of the disease. The role of computer technologies is now increasing in the diagnostic procedures. Here, in the current study we have applied the artificial neutral network (ANN) that predicted the TB disease based on the TB suspect data. Methods: We developed an approach for prediction of TB, based on artificial neural network (ANN). The data was collected from the TB suspects, guardians or care takers along with sample, referred by TB units and health centers. All the samples were processed and cultured. Data was trained on 12636 records of TB patients, collected during the years, 2016 and 2017 from provincial tuberculosis reference laboratory, Khyber Pakhtunkhwa, Pakistan. The training and test set of the suspect data were kept as 70% and 30% respectively followed by validation and normalization. The ANN take the TB suspects information’s like gender, age, HIV-status, previous TB history, sample type, sign and symptoms for TB prediction. Results: Based on TB patient’s data, ANN accurately predicted the MTB positive or negative with overall accuracy of >94%. Further, the test and validation accuracies were found >93%. This increased accuracy of ANN in detection of TB suspected patients might be useful for early management of disease to adopt some control measure in further transmission and reduce the drug resistance burden. Conclusion: ANNs algorithms may play effective role in early diagnosis of TB disease that might be applied as a supportive tool. Modern computer technologies should be trained in the diagnostics for a rapid management of disease. Delays in TB diagnosis and initiation treatment may allow the emergence of new cases by transmission, causing high drug resistance in TB high burden countries.