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Identifying Poor Performance Dimensions And Clustering Poor Performers: A Case Study In Tehran University Of Medical Sciences
Seyed Mohsen Tabatabaei , Masumeh Habibi Baghi, Seyedeh Bahareh Kashian, Mahmood Biglar,
Volume 9, Issue 5 (2-2016)
Abstract

Background and Aim: Employees are an organization's greatest assets and organizational performance is dependent to employee’s performance. Presence of inefficient employees can make other employees to be less productive. To improve inefficient employees to high performance level, it is necessary to analyze the performance of employees. This study aims to identify and determine poor performance dimensions and cluster inefficient staffs.

Materials and Methods: This study was an analytical and descriptive research. The research made questionnaire developed for data collection and Principal Component Analysis (PCA) and Cluster Analysis (CA) techniques in SPSS used to analyze the research data.

Results: The PCA results showed that six poor performance dimensions were behavioral problems, low results, lack of self-efficacy and creativity, sabotage, postponing, and individualism. The CA results declared that poor performers can be classified to five clusters include poor behavior, lazy, jobber, poor ability, marginal, managers believed that root of employees’ in inefficiency attributed jobber, poor ability, and lazy employees to internal causes, and attributed bad behavior and marginal employees to external causes.

Conclusion: The type of inefficiency and its dimensions should be identified in order to make effective decisions for inefficient employees. Employees clustering propose a new attitude toward inefficiency differentiation comparing to literature,  and this five group clustering based on empirical data expected to be more applicable in practice.


Comparison of Data Mining Algorithms' Efficiency in Thyroid Disease Diagnosis
Mohsen Rezaei, Nazanin Zahra Jafari, Hossein Ghaffarian, Masoud Khosravi Farmad3, Iman Zabbah, Parvaneh Dehghan,
Volume 13, Issue 5 (1-2020)
Abstract
Background and Aim: Timely diagnosis and treatment of abnormal thyroid function can reduce the mortality associated with this disease. However, lack of timely diagnosis will have irreversible complications for the patient. Using data mining techniques, the aim of this study is to determine the status of the thyroid gland in terms of normality, hyperthyroidism or hypothyroidism.
Materials and Methods: Using supervised and unsupervised methods after data preprocessing, predictive modeling was performed to classify thyroid disease. This is an analytical study and its dataset contains 215 independent records based on 5 continuous features retrieved from the UCI machine learning data reference.
Results: In supervised method, multilayer perception(MLP), learning vector quantization(LVQ), and fuzzy neural network(FNN) were used; and in unsupervised method, fuzzy clustering was employed. Besides, these precision figures(0.055, 0.274, 0.012 and 1.031) were obtained by root mean square error(RMSE) method, respectively.
Conclusion: Reducing the diagnosis error of thyroid disease was one of the goals of researchers. Using data mining techniques can help reduce this error. In this study, thyroid disease was diagnosed by different pattern recognition methods. The results show that the fuzzy neural network(FNN) has the least error rate and the highest accuracy.

Asthma Control Level Assessment by Moving from the Current Reactive Care Models into a Preventive Approach based on Fuzzy Clustering and Classification Algorithms
Roghaye Khasha, Mohammad Mahdi Sepehri, Nasrin Taherkhani,
Volume 14, Issue 3 (7-2020)
Abstract
Background and Aim: Asthma is a common and chronic disease of respiratory tracts. The best way to treat Asthma is to control it. Experts of this field suggest the continues monitoring on Asthma symptoms and adjustment of self-care plan with offering the preventive treatment program to have desired control over Asthma. Presenting these plans by the physician is set based on the control level in which the patient is. Therefore, successful recognition and classification of the disease control level can play an important role in presenting the treatment program to the patient and improves the self-care and strengthens the early interventions to alleviate the Asthma symptoms.  
Materials and Methods: Based on this objective, we collected the data of 96 Asthma patients within a 9-month period from a specialized hospital for pulmonary diseases in Tehran. Then we classified the Asthma control level by fuzzy clustering and different types of data mining method within a multivariate dataset with the multi-class response variable.
Results: Our best model resulting from the balancing operations and feature selection on data have yielded the accuracy of 88%.
Conclusion: Our proposed model can be applied in electronic Asthma self-care systems to support the decision in real time and personalized warnings on the possible deterioration of Asthma control. Such tools can centralize the Asthma treatment from the current reactive care models into a preventive approach in which the physician’s decisions and therapeutic actions are resulting from the personal patterns of chronic Asthma control and prevention of acute Asthma.

Analysis of the Status of Research Outputs in the Field of Knowledge Management in the Health and Healthcare Field
Mahnaz Kamani, Nooshin Soleymani Asl, Ali Mansouri,
Volume 19, Issue 3 (9-2025)
Abstract
Background and Aim: The expansion of information technology has led to the production of increasing knowledge, which may be a part of this knowledge that is hidden, so the role of knowledge management is very important to reveal knowledge. On the other hand, in health research, which is basically based on the needs of patients, their caregivers, and specialists, knowledge management is of great importance for the quality of their services. The aim of the current research is to analyze the status of research outputs in the field of knowledge management in the health sector.
Materials and Methods: Based on its nature, the present study is descriptive, quantitative, and applied, and was conducted using a lexical co-occurrence scientometric technique. The research community includes 2487 sources, which are the results of all research outputs in the field of knowledge management in the health sector, which are indexed in the Web of Science database. The analysis of the research questions was done through Excel, BibExcel, and VOSviewer software.
Results: According to research findings, the continents of Europe, Asia, and North America, respectively, have had the highest contributions to research output in the field of knowledge management in the health and healthcare sector. Among individual countries, the United States, the United Kingdom, and Canada demonstrated the most significant activity in this area, while Iran ranked 17th. Among the United Nations Sustainable Development Goals (SDGs), the goals of Good Health and Well-being, Industry, Innovation and Infrastructure, and Quality Education have received the most attention in knowledge management research related to health and healthcare. The keyword co-occurrence map highlights the prominence of terms such as “knowledge management,” “healthcare,” and “electronic health records.” The identified thematic clusters also underscore the significance of three key domains: organizational performance, information management, and health information systems.
Conclusion: In developed countries and the first level of the world, attention to knowledge management in the field of health and health is more prominent. Also, in order to achieve a high level in the field of health and health as an important and effective criterion in most development sectors, it is necessary to address other sustainable development goals, especially by establishing systems Knowledge management in the field of health helped to achieve important goals such as eradicating poverty and hunger and reducing inequalities.

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