Search published articles


Showing 2 results for Hadizadeh
Diagnosing Breast Cancer by Machine Learning
Mr Kasra Dolatkhahi, Adel Azar, Tooraj Karimi, Mohammad Hadizadeh,
Volume 15, Issue 4 (oct & Nov 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.
 

Assessing Knowledge of Pelvic Floor Disorders in Women Referring to Clinics of Teaching Hospitals of Mashhad University of Medical Sciences in 2024 (Descriptive Cross-Sectional Study)
Taraneh Mohajeri, Talaat Khadivzadeh, Fatemeh Hadizadeh-Talasaz, Zahra Hadizadeh-Talasaz, Negar Sangsefidi,
Volume 19, Issue 4 (11-2025)
Abstract
Background and Aim: Lack of awareness and inadequate knowledge about pelvic floor dysfunction (PFD), its nature, complications, prevention, and available treatments leads to increased anxiety, exacerbation of disease symptoms, and decreased quality of life. Given the high prevalence of pelvic floor disorders, this study aimed to determine the knowledge of pelvic floor disorders in women referring to clinics of teaching hospitals of Mashhad University of Medical Sciences.
Materials and Methods: A descriptive-cross-sectional study was conducted in the women’s clinics of teaching hospitals of Mashhad University of Medical Sciences in the period from July and December 2024. 205 people were included in the study using a convenience sampling method with inclusion criteria. Eligibility criteria included Iranian women over 18 years who spoke Persian and could read and write, and who provided consent to participate in the study. Participants who completed the questionnaires incompletely (more than 20% of questions unanswered) were excluded from the study. The data collection tool was a demographic and prolapse and incontinence knowledge questionnaire (PIKQ). Data analysis was performed using SPSS  after checking for normality with Kolmogorov-Smirnov.
Results: The median age of the participants was 41. The median knowledge in the field of pelvic organ prolapse was 9 (total score range from 0 to 12), and in the field of urinary incontinence was 8 (total score range from 0 to 12), and the median overall score of the questionnaire was 17 (total score range from 0 to 24), and an interquartile range of 7. 98. (47.8%) of the women had poor and moderate knowledge, and 107 (52.2%) had good knowledge. The highest level of knowledge was related to the etiology domain, while the lowest was related to the diagnosis domain. An analysis of the relationship between demographic variables and the knowledge of pelvic floor disorders showed that, overall, there was no significant association between age (P=0.60), history of pelvic floor disorders (P=0.73), number of deliveries (P=0.67), and mode of delivery (P=0.37) with the knowledge score of pelvic floor disorders.The majority of participants (106 individuals, 51.7%) obtained their required health information from physicians, midwives, or other healthcare providers.
Conclusion: The results of this study showed that almost half of the participants had poor to moderate levels of knowledge, and the lowest level of knowledge among participants with both disorders was in the areas of diagnosis and treatment. It is recommended that educational programs be designed and implemented to increase the awareness of women in the community about pelvic floor disorders. Additionally, the use of a pelvic floor disorders knowledge questionnaire may aid physicians in monitoring educational and therapeutic interventions for patients and ensuring that they receive the information necessary to manage their condition effectively.

Page 1 from 1     

© 2026 , Tehran University of Medical Sciences, CC BY-NC 4.0

Designed & Developed by: Yektaweb