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Showing 4 results for Safaei
Requirements Specification of Creating a Hybrid Model of PHR Based on Social Networking
Mozhgan Tanhapour, Ali Asghar Safaei,
Volume 11, Issue 6 (3-2018)
Abstract
Background and Aim: Personal Health Record (PHR) systems play a key role in employing patient-centered care. Besides, the inclination to use Internet services has increased in recent years. The goal of this study is to describe the needed requirements for developing the proposed hybrid PHR model in a social network. 
Materials and Methods: Using a descriptive study, a hybrid PHR model was designed in this paper to be implemented in the social health network. Then, by using observation, introspection and questionnaire methods, the requirements of the proposed social network were listed. The elicited requirements were then analyzed by SPSS 16 using descriptive statistics (absolute frequency). Finally, the proposed system was described in the Software Requirement Specification (SRS) standard format. 
Results: The proposed hybrid PHR model has benefits of all existing PHR models and is most consistent with PHR definition (individuals control and manage their PHR). It is also applicable and reliable both for individuals and physicians. The results indicated that the proposed system had PHR capabilities as well as social network functionalities. So, the possibility of creating relations between individuals provided more benefits in comparison to other PHR models.    
Conclusion: By providing reliable information, the social health network can improve patient-physician relationships. As a result, the proposed social health network can make possible the utilization of web 2.0 and social network capabilities in the healthcare field as well as the benefits of PHR records and patient-centered care.

The Usages of Body Sensor Networks: Review on References
Sorayya Rezayi , Ali Asghar Safaei , Nilofar Mohammadzadeh ,
Volume 11, Issue 6 (3-2018)
Abstract
Background and Aim: Nowadays, one of the most important areas of application of information technology in the health sector is monitoring patients' condition. Recently utilization of body area sensor networks in healthcare had significant advances. The purpose of this article is to examine the applications of wireless health sensor networks in the field of health. 
Materials and Methods: This study was a review study which was done by searching in reliable scientific sources such as Pubmed, IEEE, Science Direct, Springer and other Persian information sources like Magiran and Sid. In order to search English sources, keywords such as “Wearable and implantable body sensors” “Body area sensor network”, and in order to search in Persian sources, keywords such as “implantable and wearable network nodes”, were used.
Results: The tasks of the body sensor networks are to monitor the important parameters of the body, which are vital signs of ill health and illness. Additionally, various types of sensor networks can control various illnesses, for example, heart disease, neoplasms, diabetes, kidney disease, Parkinson's disease, infectious diseases, and so on. Also a variety of wireless body sensor networks in the medical field are divided into two main categories: the wearable wireless body area networks and the implantable wireless body area network.
Conclusion: The use of body sensor networks has a tremendous impact on health and leads to improvements in the life quality and comfort of patients. These technologies are improving, and their development aims to help patients, doctors and the treatment team.

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. 

Explaining Factors Affecting the Effectiveness of Self-Care Education Interventions in Cardiac Patients
Lia Mirsafaei, Hassan Kaviani,
Volume 13, Issue 6 (Feb & Mar 2020)
Abstract
Background and Aim: Given the increasing research, the purpose of this study was to explain the effectiveness of this training and its effective factors.
Materials and Methods: The present study is a mixed and explanatory project. In the first step to obtain the effectiveness of self-care education through quantitative meta-analysis and secondly to examine its effective factors the qualitative method of the case study was used. Statistical population of the first stage includes all relevant internal research and secondly, it included all cardiologists in Isfahan province. The data gathering tool is firstly a researcher-made checklist and for the second stage, the semi-structured interview method was used. To analyze the first stage data Comprehensive statistical meta-analysis software CMA Version II and for the second step, coding methods were used.
Results: The results showed that self-care education interventions were highly effective in cardiac patients(ES=1.616, P<0.05) In other words, the average effectiveness of self-care education in (experimental groups) 94% were more effective than control groups. On the other hand, the results of the second stage showed Factors affecting effectiveness include seven factors: education, personal control, physical activity, nutrition, emotion control, optimism, and continuous follow-up.
Conclusion: Heart disease self-care based on the above mentioned factors, as the most effective factor in controlling and improving heart disease this will lead to a longer life expectancy and a better quality of life for patients with heart disease.

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