Volume 13, Issue 6 (Feb & Mar 2020)                   payavard 2020, 13(6): 419-428 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Abbasi Hasanabadi N, Firouzi Jahantigh F, Tabarsi P. Diagnosis of Pulmonary Tuberculosis Using Artificial Intelligence (Naive Bayes Algorithm). payavard. 2020; 13 (6) :419-428
URL: http://payavard.tums.ac.ir/article-1-6909-en.html
1- Master of Science in Industrial Engineering, Department of Industrial Engineering, Faculty of Engineering Shahid Nikbakht, Sistan and Baluchestan University, Zahedan, Iran
2- Associate Professor, Department of Industrial Engineering, Faculty of Engineering Shahid Nikbakht, Sistan and Baluchestan University, Zahedan, Iran , Firouzi@eng.usb.ac.ir
3- Professor, Department of Infectious, Clinical Tuberculosis and Epidemiology Research Center, Massih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Abstract:   (1299 Views)
Background and Aim: Despite the implementation of effective preventive and therapeutic programs, no significant success has been achieved in the reduction of tuberculosis. One of the reasons is the delay in diagnosis. Therefore, the creation of a diagnostic aid system can help to diagnose early Tuberculosis. The purpose of this research was to evaluate the role of the Naive Bayes algorithm as a tool for the diagnosis of pulmonary Tuberculosis.
Materials and Methods: In this practical study, the study population included Patients with TB symptoms, the study sample is recorded data of 582 individuals with primary Tuberculosis symptoms in Tehranchr('39')s Masih Daneshvari Hospital. The data of samples were investigated in two classes of pulmonary Tuberculosis and non-Tuberculosis. A Naive Bayes algorithm for screening pulmonary Tuberculosis using primary symptoms of patients has been used in Python software version 3.7.
Results: Accuracy, sensitivity and specificity after the implementation of the Naive Bayes algorithm for diagnosis of pulmonary Tuberculosis were %95.89, %93.59 and %98.53, respectively, and the Area under curve was calculated %98.91.
Conclusion: The performance of a Naive Bayes model for diagnosis of pulmonary Tuberculosis is accurate. This system can be used to help the patient and manage illness in remote areas with limited access to laboratory resources and healthcare professional and cause to diagnose early Tuberculosis. It can also lead to timely and appropriate proceedings to control the transmission of TB to other people and to accelerate the recovery of the disease.
Full-Text [PDF 589 kb]   (590 Downloads)    
Type of Study: Research | Subject: Health Information Technology
ePublished: 1399/07/23

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


© 2020 All Rights Reserved | Journal of Payavard Salamat

Designed & Developed by : Yektaweb