Chronic Disease Prevention Program - Kryshchenko, Alona; Flores, Cynthia; Barroso, Terrance; Hernandez, Antonio; Huerta, Nathalie; Mora-Larscheid, Angel (CBR@CSUCI: An annual volume of community-based research, 2019)
As our population continues to grow, health professionals in the U.S. have a growing concern for the current and future population related to diabetes mellitus. Diabetes is an underlying disease 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-term effects include: “cardiovascular disease, stroke, chronic kidney disease, foot ulcers, and damage to the eyes.” Diabetes is a growing epidemic causing health professionals to research prevention methods as well as a way to diagnosis patients based on certain characteristics. As a result, the Chronic Disease Prevention Program (CDPP) provides blood sugar testing in a non-traditional setting (e.g. grocery stores, libraries, etc.). By using the CDPP data set and applying the tools of machine learning we will predict whether someone is diabetic or requires additional testing. Machine learning is a way to develop algorithms, allowing the computers to learn. The attributes that will be analyzed in the data set are: BMI group, age, gender, blood sugar, self diabetes, and whether the testing was done during fasting or randomly. These attributes were analyzed using Linear Regression to learn more about the relationship between the response variable (i.e. blood sugar) and the explanatory variable. Besides applying Linear Regression, we used Multiple Linear Regression as well a K-Nearest Neighbors, and Decision Tree.