Showing 3 results for Diagnosis
Masoud Keimasi, Ozhan Karimi, Hossein Rastian Ardestani,
Volume 12, Issue 4 (3-2015)
Abstract
Background and Aim: This study was conducted to assess customers’ perceptions of the quality of services in clinical diagnostic laboratories
Materials and Methods: Based on the SERVEIMPERF model, different dimensions of quality of services in terms of performance (current situation) and importance (weight) of each of the dimensions were assessed. A sample of 328 persons consulting Tehran clinical diagnostic laboratories, selected by stratified random sampling, was included in the study. Data were collected using a questionnaire.
Results: The customers’ scores for perceptions of the quality of services and its dimensions were higher than average. Dimensions of reliability with a mean score of 3.49 and that of empathy with a mean score of 2.73 were considered to be the highest and lowest dimensions, respectively. As regards importance of dimensions of service quality, the customers’ perceptions of assurance with a mean score of 4.448 was assessed to be higher than other dimensions, while the dimension of tangibility with a mean score of 3.983 was considered to be lower than other dimensions.
Conclusion: In can be concluded that different customers do not have the same perception of the various dimensions of quality of services and do not evaluate them in the same way. Thus, the differences should be taken into consideration when designing plans to improve the quality of services and deciding about priorities. It seems that the dimension of trust in the customers’ perceptions of the quality of services in clinical diagnostic laboratories is the most important and the first priority.
Ali Nik Farjam, Hassan Ajam, Robabeh Ansari Torghii, Hajar Alimohammadi, Yousef Alimohammadi , Elahe Hesari,
Volume 19, Issue 3 (3-2022)
Abstract
Background and Aim: The process of identifying Covid 19 cases over time (the trend) can provide valuable information about the coverage of diagnostic and screening programs over time. This study aimed to investigate the outpatient trend of Covid-19 in selected comprehensive health service centers of Tehran University of Meical Sciences, Tehran, Iran.
Materials and Methods: This was a descriptive cross-sectional study. The data collected inculded the number of referalls and Polymerase Chain Reaction (PCR)-positive individuals between April 13 and December 25, 2020. Central and dispersion indices (mean, median, standard deviation and interquartile range) were used to describe quantitative variables. In addition, linear and bar charts were used to describe the trend of the variables over time. All analyses were performed using the Excel 2016 and SPSS 22 software.
Results: The highest numbers of suspected cases of Covid-19 were found to be in April, June and October. There were 2 peaks in the trend of positive cases of Covid 19, and the highest proportions of daily positive cases of Covid 19 was seen in late June and early July, as well as in late September, October, and December. The highest numbers of individuals referred and tested were observed in the South of Tehran Health Center.
Conclusion: Considering the occurrence of two epidemic peaks during the study period, the occurrence of further epidemic peaks is almost certain to occur if there is no proper planning for public health services and primary health care by the responsible health authorities and policy-makers.
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.
