Tehran University Medical Journal

Background: The pandemic of Covid-19 is spreading around the world. Extensive research is needed to focus on identifying the underlying causes of the disease. This study aimed to investigate the clinical and etiological symptoms of the Covid-19.
Methods: This descriptive-analytical study, conducted on 510 infected patients in the infectious disease clinic of Imam Khomeini Hospital in Tehran from March 2019 to June 2020 for A period of Four months during the first wave of Covid-19 pandemic. The method of selecting patients was continuous and was divided into two groups of 179 inpatients and 331 outpatients based on lung scan and clinical symptoms. Demographic information, clinical signs, and risk factors were collected through a questionnaire and the data were statistically analyzed.

Conclusion: Fever, chills and cough were important symptoms in identifying infected patients with Covid-19. According to the results of the present study and the findings of other studies, the supportive effect of vitamin D3 in reducing the severity of infectious diseases should be considered. Clinical trials with appropriate sample size are recommended to investigate the functional role of this vitamin in Reducing the severity of viral diseases of the respiratory tract.

Background: The clinical field has vast sick data that has not been analyzed. Discovering a way to analyze this raw data and turn it into an information treasure can save many lives. Using data mining methods is an efficient way to analyze this large amount of raw data. It can predict the future with accurate knowledge of the past, providing new insights into disease diagnosis and prevention. Several data mining methods exist but finding a suitable one is very important. Today, coronavirus disease (COVID-19) has become one of the causing deadly diseases in the world. The early diagnosis of pandemic coronavirus disease has a significant impact in preventing death. This study aims to extract the key indications of the disease and find the best data mining methods that enhance the accuracy of coronavirus disease diagnosis.
Methods: In this study, to obtain high accuracy in diagnosing COVID-19 disease, a complete and effective workflow over data mining methods was proposed, which includes these steps: data pre-analyzing, indication selection, model creation, the measure of performance, and display of results. Data and related indications of patients with COVID-19 were collected from Kerman Afzalipour Hospital and Rafsanjan, Ali Ebn Abi Taleb Hospital. Prediction structures were made and tested via different combinations of the disease indications and seven data mining methods. To discover the best key indications, three criteria including accuracy, validation and F-value were applied and to discover the best data mining methods, accuracy and validation criteria were considered. For each data mining method, the criteria were measured independently and all results were reported for analysis. Finally, the best key indications and data mining methods that can diagnose COVID-19 disease with high accuracy were extracted.