Artificial Neural Networks for Prediction of Tuberculosis Disease

Muhammad Tahir Khan; Aman Chandra Kaushik; Linxiang Ji; Shaukat Iqbal Malik; Sajid Ali; Dong-Qing Wei

doi:10.3389/fmicb.2019.00395

Artificial Neural Networks for Prediction of Tuberculosis Disease

Front Microbiol. 2019 Mar 4:10:395. doi: 10.3389/fmicb.2019.00395. eCollection 2019.

Authors

Muhammad Tahir Khan^{1

2}, Aman Chandra Kaushik², Linxiang Ji³, Shaukat Iqbal Malik¹, Sajid Ali⁴, Dong-Qing Wei²

Affiliations

¹ Department of Bioinformatics and Biosciences, Capital University of Science and Technology, Islamabad, Pakistan.
² College of Life Sciences and Biotechnology, The State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, China.
³ Department of Physics, Thompson Rivers University, Kamloops, BC, Canada.
⁴ Provincial Tuberculosis Reference Laboratory, Hayatabad Medical Complex, Peshawar, Pakistan.

Abstract

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 in diagnostic procedure, during which there are more chances in the transmission of disease. Further, the Xpert MTB/RIF assay offers a fast diagnostic facility within 2 h, but due to low 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 neural network (ANN) that predicted the TB disease based on the TB suspect data. Methods: We developed an approach for prediction of TB, based on an ANN. The data was collected from the TB suspects, guardians or care takers along with samples, referred by TB units and health centers. All the samples were processed and cultured. Data was trained on 12,636 records of TB patients, collected during the years 2016 and 2017 from the provincial TB 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 takes the TB suspect's information such as gender, age, HIV-status, previous TB history, sample type, and signs and symptoms for TB prediction. Results: Based on TB patient data, ANN accurately predicted the Mycobacterium tuberculosis (MTB) positive or negative with an overall accuracy of >94%. Further, the accuracy of the test and validation were found to be >93%. This increased accuracy of ANN in the detection of TB suspected patients might be useful for early management of disease to adopt some control measures in further transmission and reduce the drug resistance burden. Conclusion: ANNs algorithms may play an effective role in the early diagnosis of TB disease that might be applied as a supportive tool. Modern computer technologies should be trained in diagnostics for rapid disease management. Delays in TB diagnosis and initiation treatment may allow the emergence of new cases by transmission, causing high drug resistance in countries with a high TB burden.

Keywords: ANN; TB; data; diagnosis; drug resistance.