Diabetic Risk Prediction Using Machine Learning

Abstract

The global prevalence of Type 2 diabetes has risen dramatically in recent years, posing a serious public health risk. Long-term risk prediction is an important technique for evaluating who is most likely to develop type 2 diabetes. Early detection and response can lead to better management and prevention of diabetes complications. Developing a user-friendly Windows program for long-term Type 2 diabetes risk prediction could revolutionize preventive healthcare due to technological improvements and ubiquitous smartphone use. The Machine Learning Application for long- term Type 2 And Type 1 diabetes risk predic-tion presented in this study addresses the escalating global health crisis by leveraging technology to empower individuals in managing their health. This innovative application employs advanced predic- tive algorithms to offer personalized risk assessments based on user- input data such as age, gender, family history, and lifestyle choices. The user-friendly interface ensures seamless navigation, enabling users to access educational content, set health reminders, and track their progress over time. By fostering a sense of community through peer support features, the application promotes informed decision- making andencourages positive lifestyle changes.

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