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Showing 4 results for Zahmatkeshan
Information Technology(IT): A New Revolution In Urban Health Development
Reza Safdari, Marjan Ghazi Saeedi, Maryam Zahmatkeshan,
Volume 6, Issue 3 (7 2012)
Abstract

Background and Aim: Urban health is one of the challenges of the 21st century. Rapid growth and expanding urbanization have implications for health. In this regard, information technology can remove a large number of modern cities' problems. Therefore, the present article aims to study modern information technologies in the development of urban health.

Materials and Methods: This is a review article based on library research and Internet searches on valid websites such as Science Direct, Magiran, Springer and advanced searches in Google. Some 164 domestic and foreign texts were studied on such topics as the application of ICT tools including cell phones and wireless tools, GIS, and RFID in the field of urban health in 2011. Finally, 30 sources were used.

Conclusion: Information and communication technologies play an important role in improving people's health and enhancing the quality of their lives. Effective utilization of information and communication technologies requires the identification of opportunities and constraints, and the formulation of appropriate planning principles with regard to social and economic factors together with preparing the technological, communication and telecommunications, legal and administrative infrastructure


Diagnosis of Breast Cancer Using Decision Tree, Artificial Neural Network and Naive Bayes to Provide a Native Model for Fars Province
Azita Yazdani, Ali Asghar Safaei, Reza Safdari, Maryam Zahmatkeshan,
Volume 13, Issue 3 (Aug & Sep 2019)
Abstract
Background and Aim: Breast cancer is the most common type of cancer and the main cause of death from cancer in women worldwide. Technologies such as data mining, have enabled experts in this area to improve decision making in the early diagnosis of the disease. Therefore, the purpose of this research is to develop an automatic diagnostic model for breast cancer by employing data mining methods and selecting the model with the highest accuracy of diagnosis.
Materials and Methods: In this study, 654 available patient records of Motahari breast cancer Clinic in Shiraz" were used as the sample. The number of records was reduced to 621 after the pre-processing operation. These samples had 22 features that ultimately used ten were used as effective features in the design of the model. Three types of Decision tree, Naive Bayes and Artificial neural network were used for diagnosis of breast cancer and 10-fold cross-validation method for constructing and evaluating the model on the collected data set.
Results: The results of the three techniques mentioned all three models showed promising results in detecting breast cancer. Finally, the artificial neural network accounted for the highest accuracy of 94/49%(sensitivity 96/19%, specificity 86/36%) in the diagnosis of breast cancer.
Conclusion:  Based on the results of the decision tree, the risk factors such as age, weight, Age of menstruation, menopause, OCP of records duration, and the age of the first pregnancy were among the factors affecting the incidence of breast cancer in women. 

Service-Oriented Architecture as an Integration and Scalability Strategy of Clinical Decision Support Systems
Azita Yazdani, Reza Safdari, Roxana Sharifian, Maryam Zahmatkeshan, Marjan Ghazi Saeedi,
Volume 14, Issue 2 (Jun & Jul 2020)
Abstract
Background and Aim: When clinical decision support systems are developed, implementing solutions that enable these systems to be -used on a large scale can reduce the production costs associated with the creation, maintenance and by sharing these systems, producing multiple clinical decision support systems will be prevented. In recent years, one of the approaches used for this purpose in combination with clinical decision support systems is the service-oriented architecture approach. The purpose of this study was to investigate the role and importance of service-oriented architecture in delivering scalable architectures of clinical decision support systems focusing on different approaches to this architecture.
Materials and Methods: This article is a simple review article. Bibliographic databases of IEEE Explore, Science Direct, Springer, Web of Science, and Scopus were reviewed. The keywords "Service Oriented Architecture" and "clinical decision support systems" were used as keywords along with related terms for searching these databases.
Results: The clinical decision support systems based on service-oriented architecture brings benefits such as Facilitate knowledge maintenance, reducing costs and improving agility. Point-to-point communication, enterprise service bus, service registry, clinical and engine guiding engine, and service choreography and orchestration are general architectural designs that are evident in the use of web-based clinical decision support systems based on a service-oriented architecture approach.
Conclusion: Service-oriented architecture is a potential solution for delivering scalable platforms for clinical decision systems.

Investigation of Artificial Intelligence Techniques for the Management of Cataract Disease: A Systematic Review
Zahra Karbasi, Michaeel Motaghi Niko, Maryam Zahmatkeshan,
Volume 18, Issue 3 (7-2024)
Abstract
Background and Aim: Cataracts are recognized as the cause of 51% of blindness worldwide. Following the promising initial results of artificial intelligence systems in eye diseases, AI algorithms have been applied in the diagnosis of cataracts, grading the severity of cataracts, intraocular lens calculations, and even as an assistive tool in cataract surgery. This study presents a systematic review of AI techniques in the management of cataract disease.
Materials and Methods: This systematic review study was conducted to investigate artificial intelligence techniques to manage cataract disease until November 11, 2023, and based on PRISMA guidelines. We retrieved all relevant articles published in English through a systematic search of PubMed, Scopus, and Web of Science online databases.
Results: In our initial search, 192 records were identified in the databases, and eventually, 23 articles were selected for review. The results indicated that convolutional neural network algorithms (6 articles), recurrent neural networks (1 article), deep convolutional networks (1 article), support vector machines (2 articles), transfer learning (1 article), decision trees (4 articles), random forests (4 articles), logistic regression (3 articles), Bayesian algorithms (3 articles), XGBoost (3 articles), and K-nearest neighbors clustering algorithms (2 articles) were the artificial neural network and machine learning techniques and algorithms utilized. These techniques were employed in the studies for the diagnosis (70%), management (17%), and prediction (13%) of cataract disease.
Conclusion: Various artificial intelligence and machine learning techniques and algorithms can be effective and efficient in diagnosing, grading, managing, and predicting cataracts with high accuracy. In this study, deep learning techniques and convolutional neural networks have made the greatest contribution to cataract diagnosis. Deep learning techniques, decision trees, and Bayesian algorithms were involved in cataract management. Machine learning algorithms such as logistic regression, random forest, artificial neural network, decision tree, K1-nearest neighbor, XGBoost, and adaptive boosting also played a role in cataract prediction. Just as early prediction, diagnosis, and timely referral can reduce future complications of the disease, the use of systems based on artificial intelligence models that have acceptable accuracy can be effective in supporting the decision-making process of physicians and managing this disease.

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