@article{Kryshchenko_Flores_Barroso_Hernandez_Huerta_Mora-Larscheid_2019, title={Chronic Disease Prevention Program}, volume={1}, url={https://journals.calstate.edu/cbrci/article/view/2920}, abstractNote={<p>As our population continues to grow, health professionals in the U.S. have a growing concern<br>for the current and future population related to diabetes mellitus. Diabetes is an underlying<br>disease that occurs when one’s blood sugar level is too high for a prolonged period of time.(1)<br>When untreated, short-term and long-term effects are detrimental. Acute complications include:<br>“diabetic ketoacidosis, hyperosmolar hyperglycemic state, or death.” (3) Moreover, the long-term<br>effects include: “cardiovascular disease, stroke, chronic kidney disease, foot ulcers, and damage to<br>the eyes.”<br>Diabetes is a growing epidemic causing health professionals to research prevention methods as<br>well as a way to diagnosis patients based on certain characteristics. As a result, the Chronic Disease<br>Prevention Program (CDPP) provides blood sugar testing in a non-traditional setting (e.g. grocery<br>stores, libraries, etc.). By using the CDPP data set and applying the tools of machine learning<br>we will predict whether someone is diabetic or requires additional testing. Machine learning is a<br>way to develop algorithms, allowing the computers to learn. The attributes that will be analyzed<br>in the data set are: BMI group, age, gender, blood sugar, self diabetes, and whether the testing<br>was done during fasting or randomly. These attributes were analyzed using Linear Regression<br>to learn more about the relationship between the response variable (i.e. blood sugar) and the<br>explanatory variable. Besides applying Linear Regression, we used Multiple Linear Regression as<br>well a K-Nearest Neighbors, and Decision Tree.</p&gt;}, journal={CBR@CSUCI: An annual volume of community-based research}, author={Kryshchenko, Alona and Flores, Cynthia and Barroso, Terrance and Hernandez, Antonio and Huerta, Nathalie and Mora-Larscheid, Angel}, year={2019}, month={May} }