Background: Machine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today’s computers to have the property of learning. Machine learning is gradually growing and becoming a critical approach in many domains such as health, education, and business. Methods: In this paper, we applied machine learning to the diabetes dataset with the aim of recognizing patterns and combinations of factors that characterizes or explain re-admission among diabetes patients. The classifiers used include Linear Discriminant Analysis, Random Forest, k–Nearest Neighbor, Naïve Bayes, J48 and Support vector machine. Results: Of the 100,000 cases, 78,363 were diabetic and over 47% were readmitted.Based on the classes that models produced, diabetic patients who are more likely to be readmitted are either women, or Caucasians, or outpatients, or those who undergo less rigorous lab procedures, treatment procedures, or those who receive less medication, and are thus discharged without proper improvements or administration of insulin despite having been tested positive for HbA1c. Conclusion: Diabetic patients who do not undergo vigorous lab assessments, diagnosis, medications are more likely to be readmitted when discharged without improvements and without receiving insulin administration, especially if they are women, Caucasians, or both.

This paper aims to identify the main factors that influence health related users' acceptance to mobile health services technology as a mean for receiving general health services. This study proposes Technology Acceptance Model (TAM) modified by incorporating external variables including perceived security and perceived trust in order to determine the factors that mostly influence the intention to use M-Health services. In this study, the proposed research model and hypotheses tested and validated with a data collected from the survey questionnaires distributed to a sample of 144 Emiratis and Non-Emiratis patients, general health users and health professional. In our research, we apply the frequency analysis, descriptive analysis, correlation test, t-test, Reliability & convergence and discriminant validity. The research made an indepth examination of the variables that influence the intention to use M-Health services by focusing on main stakeholders including patients, visitors and health professionals. The proposed research model is applicable as per the results achieved from the survey and it can be applied to evaluate M-Health users' acceptance and help government and non-government entities to develop m-health services that align with the needs of the users.

Obesity is one of the most serious and dangerous nutrition disorder around the world. Usually, people develop patterns of unhealthy eating that lead to increased body weight and accumulation of fat in the body. There are many reasons for that as suggested by researchers; one of the reasons is the imbalance between the energy intake from food and energy consumed in the body, which can possibly lead to increased body weight. One of the primary treatments for this serious health risk include diets, physical activity, weight-loss training and adoption of health programs that promote healthy eating. As such, reliable mechanism to prevent and control the obesity levels is vital. It led many experts and researchers who are interested in the health sector to explore more solutions that help to combat the obesity phenomenon. This paper presents a mobile health application intended to increase the awareness levels of parents and children about the obesity risks and help them to sustain balanced and healthy eating lifestyle. The proposed mobile application is an educational tool for the evaluation of interventions to prevent obesity risk levels. The application is based on the Internet-of-Things approach, which allows tracking food intake, remote capturing and constant monitoring of children data with interactive feedback displayed on the mobile application.