Journal of

Background and Aim: Work related musculoskeletal disorders shape a high percentage of physical and mental problems especially in health system work force. This study aimed to evaluate the effectiveness of ergonomic interventions by training the basics of ergonomics and exercise on neck pain in nurse staff of Tohid Hospital of Sanandaj. 
Materials and Methods: This semi-experimental and interventional study was conducted on 60 nurses of Tohid Hospital of Sanandaj who had neck pain in the last quarter of 1394 and they were chosen by random sampling method and were divided into intervention and control groups. Data were gathered using standard Modified Qswestry Neck Pain Questionnaire and pain Numeric Rating Scale. After providing ergonomics trainings and exercises to intervention group, re-evaluation was carried out and the results were compaired to the control group. Data was analyzed using Mini tab 16 software, descriptive and analytic statistics, paired t-test, the correlation coefficient and linear regression.
Results: The average age in intervention and control groups was 35 and 33 years, and the average work experience was 13 and 12.9 years respectively. There was a statistical significant relation between three variables of age, work experience and marital status with the pain intensity variable (p-value<0.05), but gender had no effect on the pain intensity (p-value=0.128).
Conclusion: Standardization of the work methods and implementing exercises, can reduce neck pain and increase physical ability of work staff, which can reduce their work absenteeism and increase their motivation.   

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.