Volume 13, Issue 6 (Feb & Mar 2020)
payavard 2020, 13(6): 419-428 |
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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
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
2- Associate Professor, Department of Industrial Engineering, Faculty of Engineering Shahid Nikbakht, Sistan and Baluchestan University, Zahedan, Iran ,
3- Professor, Department of Infectious, Clinical Tuberculosis and Epidemiology Research Center, Massih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Abstract: (4105 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 Tehran'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.
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 Tehran'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.
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