machine learning algorithm

 Bipolar disorder is a type of psychiatric disease characterized by periodic mood swings that include periods of depression and mania.

 First, three datasets related to bipolar disorder, including GSE53987, GSE35977, and GSE12679, were extracted from the PubMed database, which included 218 human samples and 9 888 458 genes. Then, genes directly related to bipolar disorder were extracted using R programming language. The shared genes were obtained from the database and extracted for 12 states with Cytoscape 3.7.1. The obtained gene expression data were trained by artificial neural network and decision tree method to identify the best models. Four parameters of sensitivity, specificity, accuracy, and area under the curve (AUC) were used to check the optimality of the model resulting from the training of machine learning algorithms.

 After R language preprocessing, 201 common genes were obtained. Then, 12 modes of 20 genes and 10 genes were extracted using the Cytohubba plugin in Cytoscape 3.7.1. The best model of 20 genes in the artificial neural network showed an AUC of 72% and the best model of 10 genes in the decision tree model showed an AUC of 78%.

 We presented two models to diagnose bipolar disorder. One model was developed using artificial neural network and tanh functions and the other model was developed using decision tree algorithm.

 The model developed by artificial neural network and the decision tree can be used in the diagnosis of bipolar disorder in order to screen conscripts who have this disorder with a high risk of relapse and exacerbation of symptoms.

The prevalence of COVID-19 has significantly impacted work environments and the workforce. Therefore, identifying the most important preventive and control strategies, as well as assessing their effectiveness, is of paramount importance. Various studies have shown that machine learning algorithms can be used to predict complex and nonlinear issues, including predicting the behavior of various diseases such as COVID-19 and the parameters affecting it, and can be beneficial. The purpose of this study has been to examine the importance of preventive measures and hygiene behaviors in preventing COVID-19 in the oil refining industry using various machine learning models.

For this purpose, demographic information and health behaviors of individuals were collected. Subsequently, a multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models were compared to enhance the analysis of the effects of preventive measures on COVID-19 infection. Finally, the most influential factors affecting the likelihood of COVID-19 infection were determined using sensitivity analysis.

The results showed that the accuracies achieved in predicting the impact of preventive measures and health behaviors on COVID-19 in occupational settings were 78.1%, 81.2%, and 78.1% by MLP, RBF, and SVM respectively. The RBF model was identified as the most accurate model for predicting the impact of health behaviors on COVID-19 disease Additionally, the level of social distancing with customers, handwashing frequency and disinfection, the availability of cleansing and disinfecting agents for hands and surfaces in the workplace, and gatherings for eating meals and snacks were identified as the most significant health behaviors influencing the prevalence of COVID-19 in the workplace.

Studies of this nature can underscore the importance of attention to preventive measures and health behaviors in unprecedented circumstances. Furthermore, the utilization of artificial intelligence models and tools such as DSS (Decision Support Systems) can serve as powerful tools for optimizing control measures in work environments.

Knee arthroplasty is one of the most important and growing surgery in orthopedics, with successful results reducing pain and increasing the patient’s mobility. The recording of this procedure is very important for the monitoring of the disease, due to the possibility of its reversibility. This research was conducted with the aim of designing, implementing and evaluating the applicability of the knee replacement surgery registration system in Tehran Shafa Yahyaian Hospital, as the orthopedic hub of the country.

Methods and Materials:

 By forming a multidisciplinary taskforce group and using the Delphi method, and after reviewing relevant texts published by orthopedic societies, the minimum required data was extracted. The modelling and evolution of the system followed the life cycle of information systems, and a prototype of the software was designed on the .NET platform. Finally, machine learning algorithms were used to analyses the primary data.

The data was divided into four categories of patient information: preoperative, intraoperative, postoperative, patient follow-up. The final version of the software was released and piloted at Shafa Hospital. The measurement of user satisfaction with the system was satisfactory. The results obtained from the implementation of the clustering algorithm were consistent with what happened at the bedside.

The development of this platform to other orthopedic centers and hospitals all over the country, adding artificial intelligence and related machine learning algorithms, will play a significant role in the decision-making of the health systems and the orthopedic communities in optimized management of this disease treatment that will lead to support patients better.