Nastaran Abbasi Hasanabadi, Farzad Firouzi Jahantigh, Payam Tabarsi,
Volume 13, Issue 6 (Feb & Mar 2020)
Abstract
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.
Setareh Talayeh, Farzad Firouzi Jahantigh, Fatemeh Bahman,
Volume 17, Issue 5 (12-2023)
Abstract
Background and Aim: The tourism industry plays a very important role in the economic cycle of society. Medical tourism, as one of the types of tourism industries, has a direct result in globalizing health care. Therefore, by strengthening the supply chain in this area, a very high added value can be achieved. For this reason, the present study provides a conceptual framework for predicting the demand for medical tourism supply chain by determining the relationship between medical tourism demand and economic, medical, and welfare-service components of Zahedan city.
Materials and Methods: The present study is a descriptive-analytical and applied research. Data were collected using a questionnaire and field and library methods. The statistical population of interest was specialist doctors in Zahedan city, and 97 people were selected using simple random sampling with Morgan’s table. The validity of the questionnaire was confirmed by experts and its reliability was obtained using Cronbach’s alpha coefficient with SPSS software more than 0.7. Data analysis was performed using the tangent sigmoid neural network algorithm, linear regression criteria, and mean square error. For this purpose, SPSS software was used to examine the correlation between the data, and MATLAB software was used to design the neural network.
Results: There was anerrore in The basis for the optimality of the answers, linear regression criteria and mean square error. The results showed that the values related to regression, education, and health were more than 0.8 and were 0.9033, 0.8818, and 0.9985, respectively. The highest priorities of the respondents related to medical equipment, education, and health were 0.5657, 0.5558, and 0.20726, respectively.
Conclusion: According to the results obtained from the proposed model, the neural network has a high accuracy in predicting the demand for medical tourism supply chain in terms of education, health, and welfare. It is also predicted that the demand for medical tourism has been constant during the one-year period of research and it is expected that medical tourism in Zahedan city will decrease in future. Therefore, it is recommended that officials pay attention to the development and improvement of medical tourism to promote it.
