AI-Powered System for Diabetes Prediction and Personalized Recommendations
Abhishek Cholakal1, Dr. S. China Venkateswarlu2, Dr. V. Siva Nagaraju3,
1Department of Electronics and Communication Engineering Institute of Aeronautical Engineering, Hyderabad, INDIA cbabhishek15@gmail.com
2Professor, Department of Electronics and Communication Engineering
Institute of Aeronautical Engineering, Hyderabad, INDIA c.venkateshwarlu@iare.ac.in
3Professor, Department of Electronics and Communication Engineering
Institute of Aeronautical Engineering, Hyderabad, INDIA v.sivanagaraju@iare.ac.in
ABSTRACT
Machine learning along with artificial intel- ligence (AI) seem to make a paradigm shift into the healthcare domain by ensuring better diagnosis and treatment approaches. Consid- ering the Mexico’s increasing prevalence of diabetes, timely diagnosis and properly deter- mined intervention approach becomes essen- tial. This project presents a unique AI-based web application that simulates and evaluates diabetes risk in individuals with the help of machine learning and generative AI, enabling personalized health recommendations. Specif- ically, the application evaluates eight param- eters such as glucose, blood pressure, BMI, age, etc. All of the parameters will allow users to foresee the risk of diabetes and determine the appropriate response to it. Moreover, the application uses Google Gemini, a generative AI model, to provide customized diabetes in- terventions by analyzing a patient’s health data, and determines likely future health sce- narios. Such interventions relate and explain the causes of diabetes, abnormal character- istics of human health, and effective dietary and lifestyle changes. The system is built using Streamlit which allows for a graphic interface that is convenient whereby the users input health data and instantly get diagnosis and interventions. It can be inferred that there is a huge opportunity in the realm of health technology advancing in terms of better pre- ventive healthcare, which results in better patient outcomes as well as better integration of machine learning and AI.
Keywords—Diabetes prediction, machine learn- ing, generative AI, healthcare analytics, Streamlit, supervised learning.
