Developing a Machine Learning Model that Predicts the Percentage of People with Diabetes in California

By: Nidhi Veerendra, Downingtown STEM Academy

This research paper is related to health data analytics in predicting the prevalence of diabetes using machine learning models. Through publicly-available health datasets, it is clear that trends in demographics, lifestyle factors, and medical history play a significant role in influencing diabetes risk. This paper also explores an original interactive dashboard visualizing health data trends and forecasted future diabetes prevalence to inform healthcare providers and policymakers in the efficient use of their resources. Through the work, it is seen that machine learning could be integrated with public health data to improve disease prevention and management strategies.

This research, recognized for its impact, ranked among the top 20 papers out of 300 participants in a competitive evaluation. Its findings underlined how important it is to leverage data-driven tools in healthcare with regard to complex challenges such as diabetes, adding to the bigger picture of how technology could revolutionize preventive medicine.