Anthropometry is a Greek word that consists of the two words “Anthropo” meaning human species and “metery” meaning measurement. It is a science that deals with the size of the body including the dimensions of different parts, the field of motion and the strength of the muscles of the body. Specific individual dimensions such as heights, widths, depths, distances, environments and curvatures are usually measured. In this article, we investigate the anthropometric characteristics of patients with chronic diseases (diabetes, hypertension, cardiovascular disease, heart attacks and strokes) and find the factors affecting these diseases and the extent of the impact of each to make the necessary planning. We have focused on cohort studies for 10047 qualified participants from Ravansar County. Machine learning provides opportunities to improve discrimination through the analysis of complex interactions between broad variables. Among the chronic diseases in this cohort study, we have used three deep neural network models for diagnosis and prognosis of the risk of type 2 diabetes mellitus (T2DM) as a case study. Usually in Artificial Intelligence for medicine tasks, Imbalanced data is an important issue in learning and ignoring that leads to false evaluation results. Also, the accuracy evaluation criterion was not appropriate for this task, because a simple model that is labeling all samples negatively has high accuracy. So, the evaluation criteria of precession, recall, AUC, and AUPRC were considered. Then, the importance of variables in general was examined to determine which features are more important in the risk of T2DM. Finally, personality feature was added, in which individual feature importance was examined. Performing by Shapley Values, the model is tuned for each patient so that it can be used for prognosis of T2DM risk for that patient. In this paper, we have focused and implemented a full pipeline of Data Creation, Data Preprocessing, Handling Imbalanced Data, Deep Learning model, true Evaluation method, Feature Importance and Individual Feature Importance. Through the results, the pipeline demonstrated competence in improving the Diagnosis and Prognosis the risk of T2DM with personalization capability.

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Educational service quality is determined by investigating the gap between students' expectations (desired status) and perceptions (status quo). This study aimed at evaluating the quality of entrepreneurship education services in universities of Kermanshah, Iran based on the SERVQUAL model. The present study is practical research in terms of objective and it is a cross-sectional survey in terms of data collection. The study population of 212 involved undergraduate students of an agricultural college who have taken an entrepreneurship course as an elective. Based on the Morgan Table 132 students were randomly investigated. Data were gathered through a standard SERVQUAL questionnaire containing 22 items about 5 dimensions of physical, responsiveness, empathy, reliability, and assurance. The results indicate that the highest service quality gap was in the responsiveness dimension (-1.916), followed by assurance and guarantee (-1.701), empathy (-1.039) and reliability dimensions (-1.015) with the smallest gap in the physical dimension (-0.718).

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