Introduction:Screening and health check-up programs are most important sanitary priorities, that should be undertaken to control dangerous diseases such as gastric cancer that affected by different factors. More than 50% of gastric cancer diagnoses are made during the advanced stage. Currently, there is no systematic approach for early diagnosis of gastric cancer.Objective:to develop a fuzzy expert system that can identify gastric cancer risk levels in individuals.Methods:This system was implemented in MATLAB software, Mamdani inference technique applied to simulate reasoning of experts in the field, a total of 67 fuzzy rules extracted as a rule-base based on medical expert’s opinion.Results:50 case scenarios were used to evaluate the system, the information of case reports is given to the system to find risk level of each case report then obtained results were compared with expert’s diagnosis. Results revealed that sensitivity was 92.1% and the specificity was 83.1%.Conclusions:The results show that is possible to develop a system that can identify High risk individuals for gastric cancer. The system can lead to earlier diagnosis, this may facilitate early treatment and reduce gastric cancer mortality rate.

Aim: Using machine learning for the early detection of this disease has an important role in improving disease indicators. Therefore, this study aims to design a disease prediction model based on data mining techniques to help in early diagnosis and provide evidence-based services. Methods: This is an applied descriptive studyconducted in 2020. The study population was all patients (800 people) referred to Taleghani Hospital in Abadan for diagnostic tests. The data were derived from the electronic records of during 2009-2010. The Spearman correlation method was used to identify the effective factors in determining the risk of CRC. Then, Binary Logistic Regression (BLR) analysis and Enter method, effective factors in determining the risk of CRC were identified. Finally, the regression prediction model for CRC was developed. SPSS 17 was used to analyze statistical data. P-value ≥ 0.05 was considered significant. Results: Eleven variables using the Spearman correlation coefficient showed a significant correlation with the output class (with and without colorectal cancer). The results of regression-logistic analysis using Enter 7 variables obtained a higher chance than other variables. The results of classifying the research samples using the Forward LR method showed that with this model, accuracy, precision, and sensitivity (91%, 93.5%, and 94.5%, respectively) had high performance. Conclusion: Designing a risk prediction model based on logistic regression plays an important role in rapid, accurate, and timely screening of patients in improving the quality of care and increasing the life expectancy of patients. The proposed model in the present study can help gastroenterologist to improve the diagnosis accuracy, precision, and effective prediction of high-risk groups.

BACKGROUND The 2019 Coronavirus disease (COVID-19) is a mysterious and highly infectious disease which was declared a pandemic by the World Health Organization (WHO). The virus poses a great threat for global health and economy. Currently, in the absence of effective treatment or vaccine, leveraging advanced digital technologies is of great importance. OBJECTIVE In this respect, Internet of Things (IoT) is useful for smart monitoring and tracing of COVID-19. Therefore, in this study, we have reviewed the literature available on the IoT-enabled solutions to tackle the current COVID-19 outbreak. METHODS This Systematic Literature Review (SLR) was conducted an electronic search of articles in the PubMed, Google Scholar, ProQuest, Scopus, Science direct, and Web of Science (WOS) databases to formulate a complete view the IoT enabled solutions to monitoring and tracing of COVID-19 according FITT model (Fit between Individual, Task, and Technology). RESULTS In the literature review, 28 articles were identified as eligible studies for analysis. This review will provide an overview of technological adoption of IoT in COVID-19 to identify significant users either primary or secondary, required technologies including technical platform, exchange, processing, storage and added value technologies and system tasks or applications in “on-body”, “in-clinic/hospital” and even “in-community” levels. CONCLUSIONS The use of IoT along with advanced intelligent and computing technologies for ubiquitous monitoring and tracking of patients in quarantine has made it a critical aspect in fighting the spread of current COVID-19 and even future pandemics.