Showing 2 results for Tarokh
Kobra Sharifiyan, Mohammadjafar Tarokh, Seyed Alireza Hashemi Golpayegani,
Volume 19, Issue 3 (3-2022)
Abstract
Background and Aim: One of the complex processes in the Ministry of Health and Medical Education in Iran is the process of registering pharmaceutical supplies. Currently the registration process is a multi-stage process, resulting in parallel services, a waste of time and unnecessary expenses. Therefore, an integrated system will improve the relevant service delivery. The purpose of this study was to identify a set of activities that could be collectively considered as a single service to create an integrated system for registering pharmaceutical supplies in the Ministry of Health and Medical Education, Iran.
Materials and Methods: This was an applied research, beginning with collecting information about the registration processes for 20 different products. In order to identify the services/steps of an integrated system for recording pharmaceutical supplies, first the Gray-Wolf multi-objective optimization (GWO) algorithm was proposed. Then the values of the algorithm parameters were extracted by the goal-based requirements analysis method and the algorithm was implemented. Finally the best services were extracted by the hierarchical analysis process.
Results: Through the proposed algorithm seven services were identified, including product class inquiry, document registration, document review according to license type and product class, licensing, laboratory services, clinical studies and payment service. These services were carefully approved with a precision of 97.3 by the experts of the Ministry of Health. The proposed framework for recording drug requirements was found to be effective and could facilitate the process by up to 90%, reduce the processing time by 80% and reduce the processing costs by 65%.
Conclusion: Creating an integrated system for registering pharmaceutical supplies is one of the important challenges of the Ministry of Health and Medical Education. This can be achieved by identifying services and combining these services to create an integrated system.
Mehrnoosh Ahangarani, Mohammad Jafar Tarokh,
Volume 22, Issue 1 (10-2024)
Abstract
Background and Aim: In recent years, machine learning and evolutionary algorithms have drawn the attention of researchers and specialists in various fields, especially in healthcare, due to their practical applications in processing large datasets to provide valuable insights. Considering the increasing prevalence of diabetes and its rapid and accurate diagnosis being one of the most critical issues in medicine, significant concerns are faced by global communities worldwide. The present study was conducted with the aim of creating a diagnostic model based on evolutionary algorithms and machine learning to diagnose diabetes.
Materials and Methods: This research based on the Indian Pima diabetes dataset presents a framework based on intelligent diabetes diagnosis. The proposed method consists of two main stages. The first stage involves a classification approach using K-nearest neighbors and random forest algorithms. The second stage includes a combined feature selection and classification approach to enhance the results of the first stage, utilizing grey wolf optimization, whale optimization, and particle swarm optimization algorithms for feature selection. Comparative analysis among different approaches is conducted through evaluation metrics such as accuracy, precision, recall, and F1-score.
Results: After comparative comparisons among the proposed models, the random forest model based on the grey wolf optimization was selected and introduced as the final model with a prediction accuracy of 81.38%.
Conclusion: The findings of this research indicate that the use of evolutionary algorithms alongside machine learning models can often enhance the efficiency and accuracy of diabetes diagnosis and its associated complications.
