Introduction: Needless to say that correct and real-time detection and effective prognosis of the COVID-19 are necessary to deliver the best possible care for patients and, accordingly, diminish the pressure on the healthcare industries. Hence our paper aims to present an intelligent algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the COVID-19 and finally opted for the best-performing algorithm. Methods: In this developmental study, the clinical data of 1703 COVID-19 and non-COVID-19 patients Using a single-center registry from February 9, 2020, to December 20, 2020, were used. The Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm identified the most relevant variables. Then, chosen features feed into the several data mining methods, including K-Nearest Neighbors, AdaBoost Classifier, Decision Tree, HistGradient Boosting Classifier, and Support Vector Machine. A 10-fold cross-validation method and six performance evaluation metrics were used to evaluate and compare these implemented algorithms, and finally, the best model was implemented. Results: Out of the 34 included features, 11 variables were selected as the essential features. The results of using ML algorithms indicated that the best performance belongs to the AdaBoost classifier with mean accuracy = 92.9%, mean specificity = 89.3%, mean sensitivity = 94.2%, mean F-measure = 91.6 %, mean KAPA = 94.3% and mean ROC = 92.1 %. Conclusion: The empirical results reveal that the Adaboost model yielded higher performance than other classification models and developed our Clinical Decision Support Systems (CDSS) interface to discriminate positive COVID-19 from negative cases.

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Background Gastric cancer is one of the leading causes of death worldwide. Screening for gastric cancer greatly relies on endoscopy and pathology biopsy, which are invasive and pose financial burdens. Thus, the prevention of the disease by modifying lifestyle-related behaviors and dietary habits or even the prevention of risk factor formation is of great importance. This study aimed to construct an inexpensive, non-invasive, fast, and high-precision diagnostic model using six machine learning (ML) algorithms to classify patients at high or low risk of developing gastric cancer by analyzing individual lifestyle factors. Methods This retrospective study used the data of 2029 individuals from the gastric cancer database of Ayatollah Taleghani Hospital in Abadan City, Iran. The data were randomly separated into training and test sets (ratio 0.7:0.3). Six  ML methods, including multilayer perceptron (MLP), support vector machine (SVM) (linear kernel), SVM (RBF kernel), k-nearest neighbors (KNN) (K = 1, 3, 7, 9), random forest (RF), and eXtreme Gradient Boosting (XGBoost), were trained to construct prognostic models before and after performing the relief feature selection method. Finally, to evaluate the models’ performance, the metrics derived from the confusion matrix were calculated via a test split and cross-validation. Results This study found 11 important influence factors for the risk of gastric cancer, such as Helicobacter pylori infection, high salt intake, and chronic atrophic gastritis, among other factors. Comparisons indicated that the XGBoost had the best performance for the risk prediction of gastric cancer. Conclusions The results suggest that based on simple baseline patient data, the ML techniques have the potential to start the prescreening of gastric cancer and identify high-risk individuals who should proceed with invasive examinations. Our model could also considerably lessen the number of cases that need endoscopic surveillance. Future studies are required to validate the efficacy of the models in a larger and multicenter population.

Introduction: Predicting acute respiratory insufficiency due to coronavirus disease 2019 (COVID-19) can diminish the severe complications and mortality associated with the disease. This study aimed to develop an intelligent system based on machine learning (ML) models for frontline clinicians to effectively triage high-risk patients and prioritize who needs mechanical intubation (MI). Materials and Methods: In this retrospective-design study, the data regarding 482 COVID-19 hospitalized patients from February 9, 2020, to July 20, 2021, was analyzed by six ML classifiers. The most critical clinical variables were identified by a minimal-redundancy-maximal-relevance (mRMR) feature selection technique. In the next step, the models' performance was assessed using confusion matrix criteria and, finally, the best model was adopted. Results: Proposed models were implemented using 23 confirmed variables. Results of comparing six selected ML algorithms indicated the extreme gradient boosting (XGBoost) classifier with 84.7% accuracy, 76.5 % specificity, 90.7% sensitivity, 85.1% f-measure, 87.4% Kappa statistic, and 85.3% for receiver operating characteristic (ROC) had the best performance in the intubation prediction. Conclusion: It is found that ML enables a satisfactory accuracy level in calculating intubation risk in COVID-19 patients. Therefore, using the ML-based intelligent models, notably the XGBoost algorithm, actually enables recognizing high-risk cases and advising correct therapeutic and supportive care by the clinicians.

Background: The rapid coronavirus disease 2019 (COVID-19) outbreak has overwhelmed many healthcare systems worldwide and put them at the edge of collapsing. As the capacity of intensive care units (ICUs) is limited, deciding on the proper allocation of required resources is crucial. Objectives: This study aimed to create a machine learning (ML)-based predictive model of ICU admission among COVID-19 in-hospital patients at the initial presentation. Methods: This retrospective study was conducted on 1225 laboratory-confirmed COVID-19 hospitalized patients during January 9, 2020 - January 20, 2021. The top clinical parameters contributing to COVID-19 ICU admission were identified based on a correlation coefficient at P-value < 0.05. Next, the predictive models were constructed using five ML algorithms. Finally, to evaluate the performances of models, the metrics derived from the confusion matrix, classification error, and receiver operating characteristic were calculated. Results: Following feature selection, a total of 11 parameters were selected as the top predictors to build the prediction models. The results showed that the best performance belonged to the random forest (RF) algorithm with the mean accuracy of 99.5%, mean specificity of 99.7%, mean sensitivity of 99.4%, Kappa metric of 95.7%, and root mean squared error of 0.015. Conclusions: The ML algorithms, particularly RF, enable a reasonable level of accuracy and certainty in predicting disease progression and ICU admission for COVID-19 patients. The proposed models have the potential to inform frontline clinicians and health authorities with quantitative tools to assess illness severity and optimize resource allocation under time-sensitive and resource-constrained situations.

Introduction: The rapid worldwide outbreak of coronavirus disease 2019 (COVID-19) has posed serious and extraordinary challenges to healthcare industries in predicting disease behavior, and outcomes. Aim: This study aimed to develop a Clinical Decision Support System (CDSS) for predicting the severity of SARS-CoV-2 infection and progression to critical illness in a patient with COVID-19 using several machine learning algorithms. Material and Methods: Using a two-center registry, the data of 2482 COVID-19 patients from February 9, 2020, to December 20, 2020, were reviewed. The Relief Feature Selection (RFS) algorithm was used for optimizing the input variables. Then, selected variables feed into ML models including XGBoost, HistGradient Boosting (HGB), Random Forest (RF), and Naïve Bayesian (NB) to construct prediction models. Afterwards, the performance of each combination was compared using some evaluation metrics. Eventually using the best ML model performance, a Clinical Decision Support System (CDSS) was implemented with C# programming language.Results: of the 63 included variables, 15 features were identified as the most important predictors. The experimental results indicated that the HGB classifier with an average classification accuracy of 94.2%, mean specificity of 92.4%, mean sensitivity of 91%, mean F-score of 87.2 %, and finally mean AUC of 87.3 % was selected as the most appropriate machine learning model for predicting the Severity of SARS-CoV-2.Conclusion: The results of this study showed that the hybrid ML algorithms and in particular the RFS-HGB (by optimizing input variables and customizing the structure of the algorithms (can help the frontline clinicians to predict the severity of COVID-19 progression.

Introduction Chronic myeloid leukemia (CML) is a myeloproliferative disorder resulting from the translocation of chromosomes 19 and 22. CML includes 15–20% of all cases of leukemia. Although bone marrow transplant and, more recently, tyrosine kinase inhibitors (TKIs) as a first-line treatment have significantly prolonged survival in CML patients, accurate prediction using available patient-level factors can be challenging. We intended to predict 5-year survival among CML patients via eight machine learning (ML) algorithms and compare their performance. Methods The data of 837 CML patients were retrospectively extracted and randomly split into training and test segments (70:30 ratio). The outcome variable was 5-year survival with potential values of alive or deceased. The dataset for the full features and important features selected by minimal redundancy maximal relevance (mRMR) feature selection were fed into eight ML techniques, including eXtreme gradient boosting (XGBoost), multilayer perceptron (MLP), pattern recognition network, k-nearest neighborhood (KNN), probabilistic neural network, support vector machine (SVM) (kernel = linear), SVM (kernel = RBF), and J-48. The scikit-learn library in Python was used to implement the models. Finally, the performance of the developed models was measured using some evaluation criteria with 95% confidence intervals (CI). Results Spleen palpable, age, and unexplained hemorrhage were identified as the top three effective features affecting CML 5-year survival. The performance of ML models using the selected-features was superior to that of the full-features dataset. Among the eight ML algorithms, SVM (kernel = RBF) had the best performance in tenfold cross-validation with an accuracy of 85.7%, specificity of 85%, sensitivity of 86%, F-measure of 87%, kappa statistic of 86.1%, and area under the curve (AUC) of 85% for the selected-features. Using the full-features dataset yielded an accuracy of 69.7%, specificity of 69.1%, sensitivity of 71.3%, F-measure of 72%, kappa statistic of 75.2%, and AUC of 70.1%. Conclusions Accurate prediction of the survival likelihood of CML patients can inform caregivers to promote patient prognostication and choose the best possible treatment path. While external validation is required, our developed models will offer customized treatment and may guide the prescription of personalized medicine for CML patients.