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This study aimed to identify diabetes risk factors in the Kurdistan Region of Iraq and explain why diabetes is rapidly spreading there, which examined some sociodemographic characteristics factors that might affect type 2 diabetes such as age, gender, alcohol consumption, and smoking, diabetes family history, and body weight. The data was collected from the hospital of diabetes named the center of diabetes in Sulaymaniyah city of Iraq, in which 218 diabetic cases were used for that purpose. According to the findings, some factors influence type 2 diabetes, such as Gender, Smoking, and Body Weight. For Gender, Females are more likely to have diabetes than males. Also, someone that smokes is more likely to have diabetes than those who do not smoke. Furthermore, with increasing each kilogram of body weight, the diabetes degree increases as well. On the other hand, regarding the results, some factors such as Age, Consumption of Alcoholic, and Diabetes Family History do not affect type 2 diabetes. Depending on the findings, it is recommended that people engage in regular physical activity and consume nutritious foods to minimize weight gain, which is one of the primary causes of diabetes as well as they should quit smoking.

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In this paper we constructed four models for hourly (4*24 models for the 24 hours are 96 models of the day) and daily (4*7 models for the 7 days are 7 models of the week) electricity load using dynamic linear models (DLM) with various parameters. The Bayesian method and Kalman filter where used to estimate the parameters of four load models (Simple DLM, Trend DLM, Trend seasonal DLM and Regression DLM) and one step ahead forecasting. In order to select the best and most efficient model for estimating and forecasting the electricity load in Erbil City, the four models were compared using (mean absolute error, mean absolute percentage error and root mean square error. The results presented in this paper based on real measured data. R-programming language and Microsoft Excel were used for data analyses. The result shows that Regression-DLM has best estimation and forecasting results compared to other models using the accuracy criteria, and the Kalman filter algorithm is a well-established technique and is suitable for estimating the parameters of load models that are used in this work as dynamic linear equations that include load signal with uncorrelated Gaussian white noise. is the most natural method to allow for this updating of information at each time t. (Durbin and S. J. Koopman 2012). Successful application of quantitative investment strategies rests on the availability of reliable forecasts of asset returns, volatilities and co-volatilities as they vary over time. Dynamic linear models (DLMs) are widely used due to flexibility in model specification, ability to changing location dynamics and predictor-outcome relationships, ability to incorporate external/intervention information, and flexibility in automatically incorporating series dependence characteristics among multiple series. So that models are updated dynamically, responding to the latest locations D

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