Showing 23 results for اصغری
Hanieh-Sadat Ejtahed, Shirin Hasani-Ranjbar, Hanieh Malmir, Azin Pakmehr, Rezvan Razmande, Yasaman Khorshidi, Golaleh Asghari, Ahmadreza Soroush, Afshin Ostovar, Bagher Larijani,
Volume 23, Issue 6 (1-2024)
Abstract
Background: Considering the increasing and alarming trend of overweight and obesity as well as its related complications, in this study, a comprehensive clinical guide for the medical care of patients with obesity was written based on the clinical recommendations of the American Endocrinology Association and the American College of Endocrinology, and it has been adjusted as much as possible based on the conditions in Iran.
Methods: A complete search was performed in the available databases without any restrictions with a specific strategy. Using the opinions of experts in this field, the best clinical guidelines were selected and obesity clinical guidelines were written for Iranian adults. Recommendations were given based on a detailed review of available clinical evidence and considering objective factors.
Results: A total of 1788 references were used and in response to 9 clinical questions, 123 recommendations, including 160 special statements, were provided to determine a comprehensive medical care program for obesity. In this article, we discuss the prevention, screening, diagnosis, benefits and goals of obesity treatment. Questions 6 to 9 regarding obesity treatment steps and its individualization will be published in the next part of the article.
Conclusion: The detailed evidence-based questions and recommendations outlined in this study identify clinical considerations that facilitate decision-making in obese patients from screening and diagnosis to goals of treatment.
Hanieh-Sadat Ejtahed, Shirin Hasani-Ranjbar, Hanieh Malmir, Rezvan Razmandeh, Azin Pakmehr, Yasaman Khorshidi, Golaleh Asghari, Amir Mohammad Mortazavian, Mohammad Reza Mohajer-Tehrani, Afshin Ostovar, Bagher Larijani,
Volume 24, Issue 1 (3-2024)
Abstract
Background: The prevalence of overweight, obesity and related complications is increasing rapidly in the world. Also, treating this disease in the presence or absence of co-morbidities has become a challenge. In this article, based on the clinical recommendations of the American Endocrinology Association and the American College of Endocrinology, a comprehensive clinical guide has been written for the stages of treating obese patients and its individualization, and it has been tried to be adjusted as much as possible based on the conditions in Iran.
Methods: with a specific search strategy, a complete search was performed in PubMed, Scopus, ISI Web of Science, EMBASE and Google Scholar Cochrane databases. Then, the best clinical guidelines suitable for the Iranian society were selected and using the opinions of specialists and clinical experts, a clinical guideline was prepared for the treatment of obesity in Iranian adults.
Results: In this article, in continuation of the previous article, we answered the questions number 4 to 6 regarding the stages of obesity treatment and its individualization in adults of Iranian society, and presented a total of 60 recommendations in this regard.
Conclusion: In this part of the clinical guide for obesity in Iranian adults, we tried to have a special view on the treatment of these patients and by providing evidence-based recommendations and statements, the treatment process was personalized as much as possible for patients with special conditions so that decision-making in this regard is facilitated for the relevant colleagues in this field.
Hossein Azgomi, Ali Asghari,
Volume 25, Issue 5 (12-2025)
Abstract
Background: Diabetes is a chronic disease where the body cannot use or store glucose properly. Diabetes occurs when the pancreas is unable to produce insulin, or the body cannot use the insulin produced. Nowadays, diabetes is a common disease worldwide, and providing automated methods for its diagnosis is critically important.
Methods: This paper introduces a novel method for diagnosing diabetes using artificial intelligence (AI) algorithms. The proposed method is based on metaheuristic and classification algorithms. The simulated annealing (SA) metaheuristic algorithm was used for feature selection. Diabetes diagnosis was performed using the improved K-nearest neighbor (KNN) classification algorithm. In addition to the proposed method, the performance of two other methods, named MVMCNN and WKNN, was studied for diabetes diagnosis.
Results: The proposed method has been compared practically with the two other methods for diagnosing diabetes. The comparisons are based on the accuracy rate of disease diagnosis. In the experiments, the proposed method (SAKNN) demonstrated 95% accuracy, the MVMCNN method showed 93% accuracy, and the WKNN method demonstrated 90% accuracy. Thus, the proposed method outperformed the others. The proposed method also had acceptable performance in terms of time and several other criteria.
Conclusion: The proposed method for diagnosing diabetes, using metaheuristic and classification algorithms, provides higher accuracy compared to other methods. These results indicate that the proper use of AI techniques can offer effective solutions for the automatic diagnosis of diabetes and can be used as an auxiliary tool for doctors and researchers.
