Real-Time Prediction of Diabetes Complications Using Regression-Based Machine Learning Models

Diabetes, frequently identified as prolonged sickness, is a set of metabolism sicknesses caused through constantly high blood sugar levels. If correct early prediction is viable, the chance of risk and bareness of diabetic patient can be significantly compact. According to several studies, diseases at early stages are more likely to be cured and prevented from becoming a life-threatening illness. Diabetes is the same in this case, with proper monitoring and controlled daily routine not only it can be prevented but also can be kept under control and avoid any serious impact on patient’s health. Proposed solution leverages various capabilities of machine learning techniques to develop a web app interface which can allow people to easily draw insights about their current likelihood of being a diabetes patient and also provides data about the factors which are affecting this prediction so that the patient can act accordingly. The web app uses a machine learning model as a backend which takes the user inputs and generates the prediction; the framework itself is trained for vast publicly available datasets and uses machine learning techniques to generate prediction. Though model also checks for the parameter which has maximum influence over the prediction and generates the recommendations using Large Language Model such as ChatGPT, the patient can adapt to the lifestyle accordingly and hence control the further decline in health. The objective of the project is to generate insights on real-time data through an interface which is easily accessible and can cater for a wide audience hence contributing toward increasing diabetes awareness and promoting healthy lifestyle which helps people to reduce the risk of being diabetic due to irregularities in the modern chaotic lifestyle. Such a system will also increase the integration of advanced computing tools in daily life allowing everyone to leverage the power of machine learning to monitor and track diabetes effectively.