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Background: The use of mobile health has a pivotal role in the prevention and treatment of many diseases. This study aimed at determining the affecting factors in acceptance of mobile health by using a modified acceptance model, among medical sciences students in the south-west of Iran. Materials and Methods: This cross-sectional, analytical study was conducted in 2017. The research population included all the students of Lorestan University of Medical Sciences (LUMS). The 352 of students selected as the samples of study through a stratified sampling method. Data gathering was done through a valid and reliable questionnaire. The data was analyzed using Linear Structural Relations (LISREL) and Statistical Package for the Social Sciences (SPSS) software. Results: The findings showed that perceived usefulness (t 7, 38 = 2.16, p = 0.03), performance expectancy (t 7, 70 = 3.18, p = 0.01), facilitating conditions (t 10, 61 = 4.17, p < 0.001), and attitude to use (t 7, 14 = 5.49, p < 0.001) were effective in the behavior intention of mobile health. Moreover, the results showed that the behavior intention of mobile health applications (t 10, 77 = 8.10, p < 0.001) is effective on its user behavior. Conclusions: The results of our study showed that perceived usefulness, performance expectancy, facilitating conditions, and attitude to use of technology were the affecting factors in the acceptance of mobile health by the students. It is suggested that the policymakers and authorities comprehensively consider these important factors when introducing new technologies.

Introduction IBS manifestations are similar to heartburn, making diagnosis difficult for physicians. To diagnose this illness, doctors now rely on their experiences and therapeutic guidelines. Misdiagnosis, added costs, and extended treatment times are possible outcomes of this method. Researchers believe CDSS can help clinicians solve problems when used to make decisions. The CDSS is used in this current study to diagnose IBS. Methods The fuzzy-logic algorithm was optimized in this applicable modeling research using particle swarm optimization (PSO). Input data, an inference engine, and output data comprised this fuzzy-logic model-based system. Classification algorithms and the PSO method were used to select the input variables. PSO and "If-then" rules were used in the inference engine to extract data from the dataset. Patients experiencing IBS and normal people make up the output. The accuracy, sensitivity, precision, specificity, confusion Matrix, kappa test, and F-measure values of this model were used to assess its performance. Results The recommended model had a mean score of 96.5% accuracy, 100% sensitivity, 95.2% precision, and 89.4% specificity. Conclusion The optimized model was found that effectively diagnosed IBS cases. To improve the accuracy of this disease's diagnosis, healthcare organizations can implement the aforementioned model into their strategic scheduling at a reasonable expense.