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Diagnosing Breast Cancer by Machine Learning
Mr Kasra Dolatkhahi, Adel Azar, Tooraj Karimi, Mohammad Hadizadeh,
Volume 15, Issue 4 (10-2021)
Abstract
Background and Aim: Cancer and in particular Breast cancer are among the diseases that have the highest mortality rate in Iran after heart disease. The accurate prognosis for Breast cancer is important, and the presence of various symptoms and features of this disease makes it difficult for doctors to diagnose. This study aimed to identify the factors affecting Breast cancer, modeling and ultimately diagnosing the risk of Breast cancer.
Materials and Methods: In the present study, first, by content analysis and library studies, the effective factors in Breast cancer were identified, then with the help of a team of experts consisting of physicians and subspecialists in Breast oncology and Breast surgery; With the help of the Delphi method, the factors were adjusted and 26 final factors that were numerically correct and string based on local and climatic conditions were approved. Then, according to the final factors and based on the medical records of 5208 patients in the Cancer Research Center of Shahid Beheshti University of medical sciences, to diagnose cancer, Decision Tree, Random Forest, and Support Vector Machine methods were used as machine learning methods.
Results: In the first step, by content analysis method, 29 effective factors in Breast cancer were identified. Then, taking into account the indigenous and climatic conditions and using the Delphi method and also using the opinions of 18 Experts during three years, 26 factors were finalized. In the final step, using the medical records of the patients and the results obtained from the three methods mentioned, random forest, had the highest accuracy of 94.75% and precision of 97.26% in diagnosing Breast cancer. It has been noted that, compared to other similar studies, indigenous databases have been exploited, the accuracy obtained has been very close to previous studies, and in many cases much better.
Conclusion: Using the random forest method and taking advantage of the factors affecting Breast cancer, the ability to diagnose cancer has been provided with greatest accuracy.
 

Proposing a Model for Managerial Competencies of Hospital Managers in Iran
Kourosh Abbasiyan, Mohammad Alimoradnori, Mohammad Bagher Karami,
Volume 18, Issue 2 (5-2024)
Abstract
Background and Aim: Managers, as the main decision-makers in facing various internal and external organizational problems, play a significant and determining role in the success or even failure of an organization. If competent and experienced managers are positioned at the top of organizations, the success of these organizations in achieving their goals will be guaranteed and an organization can achieve maximum efficiency with minimal resources. The aim of this study was to design a model of managerial competencies for hospital managers.
Materials and Methods: This qualitative research was conducted from year 2020 to 2022. After reviewing studies related to the topic, the extracted competencies were given to 19 experts consisted of relevant academic faculty members and managers with experience in the healthcare system and hospitals. Eventually, a managerial competency model was formulated through the use of the Delphi method and expert panel discussions. Collected data were analyzed in Excel software.
Results: The developed model in this research for the concept of hospital managers’ competencies includes 33 managerial competencies of hospital managers in four main management functions (planning, organizing, leadership and control) and managerial roles, which starts from literature review and performing two Delphi steps and implementing two expert panel plans. In the competency of hospital managers model, the planning dimension consists 4 components, organizing consists 4 components, leadership consists 12 components, control consists 4 components, and managerial roles consists 9 components. Strategic thinking, which is a subset of planning, has the highest weight (0.495) and highest rank among other components, and continuous improvement, which is a subset of managerial roles, has the lowest weight (0.033) and lowest rank among other components.
Conclusion: This study proposes an exclusive and comprehensive model, utilizing practical techniques as a suitable solution for evaluating the managerial competencies of hospital managers. The proposed framework in this study can serve as a standard performance assessment tool for evaluating managers.

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