TY - JOUR AU - Alona Kryshchenko AU - Cynthia Flores AU - Terrance Barroso AU - Antonio Hernandez AU - Nathalie Huerta AU - Angel Mora-Larscheid PY - 2019/05/09 Y2 - 2024/03/28 TI - Chronic Disease Prevention Program JF - CBR@CSUCI: An annual volume of community-based research JA - cbrci VL - 1 IS - SE - STEM Fields DO - UR - https://journals.calstate.edu/cbrci/article/view/2920 AB - As our population continues to grow, health professionals in the U.S. have a growing concernfor the current and future population related to diabetes mellitus. Diabetes is an underlyingdisease that occurs when one’s blood sugar level is too high for a prolonged period of time.(1)When untreated, short-term and long-term effects are detrimental. Acute complications include:“diabetic ketoacidosis, hyperosmolar hyperglycemic state, or death.” (3) Moreover, the long-termeffects include: “cardiovascular disease, stroke, chronic kidney disease, foot ulcers, and damage tothe eyes.”Diabetes is a growing epidemic causing health professionals to research prevention methods aswell as a way to diagnosis patients based on certain characteristics. As a result, the Chronic DiseasePrevention Program (CDPP) provides blood sugar testing in a non-traditional setting (e.g. grocerystores, libraries, etc.). By using the CDPP data set and applying the tools of machine learningwe will predict whether someone is diabetic or requires additional testing. Machine learning is away to develop algorithms, allowing the computers to learn. The attributes that will be analyzedin the data set are: BMI group, age, gender, blood sugar, self diabetes, and whether the testingwas done during fasting or randomly. These attributes were analyzed using Linear Regressionto learn more about the relationship between the response variable (i.e. blood sugar) and theexplanatory variable. Besides applying Linear Regression, we used Multiple Linear Regression aswell a K-Nearest Neighbors, and Decision Tree. ER -