Danavale Vaishnavi Vaishnavi,
Malusare Shamal,
Dharpale Nikita,
Gavhane Sanika,
Amol C. Jadhav,
- Student,, Shri Chhatrapati Shivaji Maharaj College of Engineering, Pune,, Maharashtra,, India
- Student,, Shri Chhatrapati Shivaji Maharaj College of Engineering, Pune,, Maharashtra,, India
- Student,, Shri Chhatrapati Shivaji Maharaj College of Engineering, Pune,, Maharashtra,, India
- Student,, Shri Chhatrapati Shivaji Maharaj College of Engineering, Pune,, Maharashtra,, India
- Student,, Shri Chhatrapati Shivaji Maharaj College of Engineering, Pune,, Maharashtra,, India
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.
Keywords: Type 2 diabetes, long-term risk prediction, personalized assessments, preventive healthcare, educational content, health track-ing, community support, technology, public health
[This article belongs to International Journal of Satellite Remote Sensing (ijsrs)]
Danavale Vaishnavi Vaishnavi, Malusare Shamal, Dharpale Nikita, Gavhane Sanika, Amol C. Jadhav. Diabetic Risk Prediction Using Machine Learning. International Journal of Satellite Remote Sensing. 2024; 02(01):11-17.
Danavale Vaishnavi Vaishnavi, Malusare Shamal, Dharpale Nikita, Gavhane Sanika, Amol C. Jadhav. Diabetic Risk Prediction Using Machine Learning. International Journal of Satellite Remote Sensing. 2024; 02(01):11-17. Available from: https://journals.stmjournals.com/ijsrs/article=2024/view=170731
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| Volume | 02 |
| Issue | 01 |
| Received | May 23, 2024 |
| Accepted | July 10, 2024 |
| Published | September 7, 2024 |