Tehran University Medical Journal

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.

Background: Phenylketonuria (PKU) is a genetic metabolic disorder that, if left untreated, leads to irreversible cognitive, behavioral, and neurological damage. Sistan and Baluchestan and West Azerbaijan provinces are among the regions that have reported high rates of the disease due to specific ethnic characteristics. This study aims to compare the frequency and pattern of clinical symptoms in the two provinces of Sistan and Baluchestan and West Azerbaijan. Methods: This cross-sectional descriptive-analytical study was conducted on 60 patients with PKU who had been referred to Imam Ali Hospital (Zahedan) and Urmia Hospital during the past ten years. Data were collected from medical records and structured interviews. Results: The mean age of the patients was 5.67 ± 6.98 years. The mean height, weight, and head circumference were 30.28 ± 113.08 cm, 13.22 ± 25.13 kg, and 1.83 ± 43.36 cm, respectively. The mean serum phenylalanine level at the time of diagnosis was 13.58 ± 14.65 mg/dL. Of the 60 patients, 31 (51.7%) were male and 29 (48.3%) were female. The difference between the two sexes in the occurrence of clinical symptoms was not statistically significant (p<0.05). Psychiatric disorders were reported in 20 (33.3%) of the patients. The prevalence of these disorders was significantly higher with increasing age (p = 0.041). Conclusion: This study indicates the existence of significant regional differences in the clinical manifestations of phenylketonuria; such that patients from Sistan and Baluchestan province experienced a higher rate of psychiatric and neurological symptoms than patients from West Azerbaijan. The severity of symptoms increased with increasing age and duration of illness, emphasizing the importance of early diagnosis and continuous therapeutic follow-up. It was also noteworthy that some patients still had severe clinical symptoms despite having lower phenylalanine levels at diagnosis. Overall, the findings of this study emphasize the need for early diagnosis, equitable access to health services, and sustained metabolic control to improve outcomes for PKU patients in the country.