Predicting Diabetes with Machine Learning Analysis of Income and Health Factors

TL;DR

This study uses machine learning to analyze how health indicators and income influence diabetes risk, revealing that lower income and certain health factors significantly increase the likelihood of diabetes.

Contribution

It introduces income as a key factor in diabetes prediction, combining socio-economic and health data with machine learning for comprehensive analysis.

Findings

Lower income correlates with higher diabetes incidence.

High blood pressure and cholesterol are significant predictors.

Income and BMI are crucial factors in diabetes risk.

Abstract

In this study, we delve into the intricate relationships between diabetes and a range of health indicators, with a particular focus on the newly added variable of income. Utilizing data from the 2015 Behavioral Risk Factor Surveillance System (BRFSS), we analyze the impact of various factors such as blood pressure, cholesterol, BMI, smoking habits, and more on the prevalence of diabetes. Our comprehensive analysis not only investigates each factor in isolation but also explores their interdependencies and collective influence on diabetes. A novel aspect of our research is the examination of income as a determinant of diabetes risk, which to the best of our knowledge has been relatively underexplored in previous studies. We employ statistical and machine learning techniques to unravel the complex interplay between socio-economic status and diabetes, providing new insights into how financial well-being influences health outcomes. Our research reveals a discernible trend where lower income brackets are associated with a higher incidence of diabetes. In analyzing a blend of 33 variables, including health factors and lifestyle choices, we identified that features such as high blood pressure, high cholesterol, cholesterol checks, income, and Body Mass Index (BMI) are of considerable significance. These elements stand out among the myriad of factors examined, suggesting that they play a pivotal role in the prevalence and management of diabetes.