Background and Objective: Colorectal cancer (CRC) is one of the most prevalent malignancies in the world. The early detection of CRC is not only a simple process, but it is also the key to its treatment. Given that data mining algorithms could be potentially useful in cancer prognosis, diagnosis, and treatment, the main focus of this study is to measure the performance of some data mining classifier algorithms in terms of predicting CRC and providing an early warning to the high-risk groups.
 Materials and Methods: This study was performed in 468 subjects (194 CRC patients and 274 non-CRC cases). We used the CRC dataset from the Imam Hospital, Sari, Iran. The Chi-square feature selection method was utilized to analyze the risk factors. Then, four popular data mining algorithms were compared based on their performance in predicting CRC, and, finally, the best algorithm was identified.
 Results: The best outcome was obtained by J-48 (F-Measure = 0.826, ROC=0.881, precision= 0.826 and sensitivity =0.827), Bayesian Net was the second-best performer (F-Measure = 0.718, ROC=0.784, precision= 0.719 and sensitivity=0.722). Random-Forest performed the third-best (F-Measure= 0.705, ROC=0.758, precision= 0.719, and sensitivity=0.712). Finally, the MLP technique performed the worst (F-Measure = 0.702, ROC=0.76, precision = 0.701 and sensitivity=0.703).                                                                      
 Conclusion: According to the results, we concluded that the J-48 could provide better insights than other proposed prediction models for clinical applications.

Background and Objective: The COVID-19 pandemic is a phenomenon that has infected and killed many people worldwide. Underlying diseases such as diabetes mellitus, heart failure, and chronic kidney disease (CKD) can affect the severity of COVID-19 and aggravate patients' condition. This study aimed to predict the severity of the COVID-19 disease in CKD patients by combining feature selection and classification methods.
Materials and Methods: This study was conducted between March 2021 and September 2021 in Isfahan University of Medical Sciences. The data set includes 83 traits of 72 kidney transplant patients, 231 kidney failure patients, and 105 dialysis patients. The data set has 77 input attributes, including age, sex, diabetes mellitus, hypertension, ischemic heart disease, chronic lung disease, and kidney transplant.
In the proposed method, the combination of ant colony algorithm and the CHAID method has been used.
Results: The combination of the ant colony algorithm and CHAID method leads to better performance than CHAID alone. A total of 22 rules were extracted, of which 6 rules with a confidence of more than 60% were introduced as selected rules. The most reliable rule states that if a person has CKD stage 5, is not undergoing dialysis (5ND), and is short of breath, in 81% of cases the type of COVID-19 disease will be severe.
Conclusion: In this study the severity of COVID-19 disease in kidney patients was measured using variables including age, diabetes mellitus, blood pressure, CKD stage, etc. The results showed that high levels of kidney disease can lead to severe COVID-19.

Background & Objective: The present study was designed to investigate the ratio of red blood cell distribution width (RDW) to serum calcium level as a predictor of acute pancreatitis severity.
Materials and Methods: In this cross-sectional study, 336 acute pancreatitis patients referred to the emergency rooms of Valiasr and Mousavi hospitals were investigated during the period from 2022 to 2023. Demographic, clinical and laboratory data were obtained from patient records. The data were analyzed using STATA16, and the area under the ROC curve was applied to determine the best cutoff point for the ratio of RDW to serum calcium level.
Results: 188 patients had mild pancreatitis and 148 had moderate/severe pancreatitis. The mean age of patients with mild and moderate/severe acute pancreatitis was 60.22 (±17.84) and 58.68 (±18.53) years, respectively. The frequency of women in mild and moderate/severe pancreatitis was 53.2% and 52%, respectively. The multiple logistic regression model showed that for every one unit increase in the mean RDW to calcium ratio, the odds of developing moderate/severe pancreatitis increased 4.30-fold (OR= 4.30 ; P<0.001). The best cutoff for the ratio of RDW to serum calcium in predicting moderate/severe acute pancreatitis was 1.69. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve for this cutoff were 83.45%, 85.47%, 85.17%, 83.77%, and 0.8698, respectively.
Conclusion: The ratio of RDW to serum calcium can be a rapid, accessible, inexpensive, sensitive, and reliable indicator for predicting the severity of acute pancreatitis.