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Showing 2 results for Abbasi Hasanabadi

Mohamad Reza Shahraki, Nastaran Abbasi Hasanabadi,
Volume 12, Issue 6 (Feb & Mar 2019)
Abstract

Background and Aim: One of the important dimensions of access to health services is uniform distribution. A survey on the distribution of health and treatment indices in different regions reveals inequalities in order to reduce inequalities. The study aims to rank the cities of Sistan and Baluchestan province from the perspective of access to healthcare services.
Materials and Methods: This is a descriptive-applied and cross-sectional study. The data were collected from the database of Statistical Center of Iran. The studied indices were weighted by Shannon entropy method, and the ranking of cities was done by TOPSIS method. The distribution of health services among the cities was shown by Spearman and Kendall correlation coefficient. Also, with the coefficient of variation, the important indices for creating imbalances were determined.
Results: The results of TOPSIS method showed a significant difference between the levels of access to health services indices. Zabol and Zahedan cities ranked first and second, and the cities of Ghasreghand, Sibsuran and Fanuj were in the last rank, respectively. Among the factors that led to a level difference, the number of PhDs, laboratory sciences doctors and rehabilitation centers caused the greatest difference between the cities. The findings show that there is little correlation between demographic rank and access to healthcare services, and health services are not distributed equitably according to the population.
Conclusion: More attention to cities that are ranked below the level of access to healthcare resources is needed. More healthcare resources should be given to this province because there are few healthcare indices relative to its population.

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.


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