Introduction: Diabetes is a disease associated with high levels of glucose in the blood. Diabetes make many kinds of complications, which also leads to a high rate of repeated admission of patients with diabetes. The aim of this study is to diagnose Diabetes with machine learning techniques.Material and Methods: The datasets of the article contain several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age. The main objective of the machine learning models is to classify of the diabetes disease.Results: six classifiers have been also adapted and compared their performance based on accuracy, F1-score, recall, precision and AUC. And Finally, Adaboost has the most accuracy 83%.Conclusion: In this paper a performance comparison of different classifier models for classifying diagnosis is done. The models considered for comparison are logistic regression, Decision Tree, support vector machine (SVM), xgboost, Random forest and ada boost. Finally, in the comparison flow, Adaboost, Logistic Regression, SVM and Random Forest, usually has had a high amount; and their amounts has little differences normally.

Introduction: Diabetes is a chronic disease associated with abnormal high levels of glucose in the blood. Diabetes make many kinds of complications, which also leads to a high rate of repeated admission of patients with diabetes. The goal of this study is to Predict hospital readmission of Diabetic patients with machine learning techniques.Material and Methods: The data used in the study are data obtained from the UCI Machine Learning Repository about diabetic patients. The dataset used contains 100,000 instances and it include 55 features from 130 hospitals in the United States for 10 years.Results: This article gets results from the final stages of evaluation. In this evaluation process, compared the performance of Decision tree, Random forest, Xgboost, k-Neighbors, adaboost and deep neural network with accuracy.Conclusion: The number of selected features by PCA-based feature selection method improve the predictive performance based on accuracy of deep learning and most machine learning models for predicting readmission. The improvement of machine learning models depended on the specific choice of the prediction model, number of selected features, and “k” for k-fold validation.

BACKGROUND Liver transplantation, the last treatment for advanced liver failure, necessitates patient education due to its wide range of complications and subsequent disabilities. OBJECTIVE This study aimed to design a mobile-based educational program to provide liver transplant patients with critical health information METHODS In the first phase of the study, the crucial educational components were collected from the literature and organized in the form of a questionnaire using library studies and available global guidelines. The validity and reliability of this researcher-made questionnaire were confirmed by a panel of experts (n=15), including gastroenterologists and liver specialists working in the Motahari liver clinic and AbuAli Sina Hospital in Shiraz. The application was designed followed by analyzing the data gathered from the first phase. To evaluate the mobile phone program’s usability, 30 liver transplant patients were provided with the newly developed mobile application. RESULTS Most educational components covered in the questionnaire were deemed necessary by experts in the first phase. As a result, the educational contents were classified under 10 categories. The application had a good level of usability since the participants’ satisfaction score was 8.1 (out of 9 points). CONCLUSIONS The mobile-based educational application can enhance the patients’ knowledge and self-care performances in post-transplant conditions by promoting the possibility of affordability and usability for liver transplant patients.

Background Celiac disease is a major public health problem in many countries, including Iran. Considering the disease’s exponential spread throughout the world and its risk factors, identifying the educational priorities and minimum data required to control and treat the disease is of great significance. Methods The present study was conducted in two phases in 2022. In the first phase, a questionnaire was developed based on the information obtained from a review of the literature. Later, the questionnaire was administered to 12 pundits in the fields of nutrition (n = 5), internal medicine (n = 4), and gastroenterology (n = 3). As a result, the necessary and important educational content was determined for developing the Celiac Self-Care System. Results According to the experts’ viewpoints, the educational needs of patients were classified into nine categories of demographic information, clinical information, long-term complications, comorbidity, tests, medications, dietary recommendations, general recommendations, technical capabilities as well as 105 subcategories. Conclusions Due to the increased prevalence of Celiac disease and the lack of an established minimum set of data, determining the required educational information is of great importance at the national level. Such information could be useful in implementing educational health programs to raise the public level of awareness. In the field of education, such contents can be employed in planning new technology based on mobile phones (mobile health), preparing registries, and producing widely used content.

Vehicular Ad hoc Networks (VANET) are expected to have great potential to improve both traffic safety and comfort in the future. When many vehicles want to access data through roadside unit, data scheduling become an important issue. In this paper, we identify some challenges in roadside based data access. To address these challenges we first review some existing scheduling schemes. We then propose a priority scheduling and finally show that using this idea can increase QOS compare to previous algorithms. KEYWORDSVANET, V2V , V2I , RSU , Packet scheduling 1.INTRODUCTIONVehicular network applications require wireless mobile communications. Currently, there are several possible paradigms for wireless mobile communication, for example, cellular, ad hoc, wireless LAN, and Info-stations [5] [6]. Clearly, the choice of technology depends on the application that the network is intended to support. For this reason we need to have a clear insight into these applications and their requirements. Integrating a network interface, GPS receiver, different sensors and on-board computer gives an opportunity to build a powerful car-safety system, capable of gathering, processing and distributing information. Numerous applications can be deployed in a network established with such equipped vehicles and proper infrastructure. These applications are either safety related or comfort related [3].