Ai for autonomous health care on diabetes diagnostics

Hari Kiran Vege, Sri Kamal Yandamuri, Jetti Vennela and Sai Venkat

SAP Health and Policy, 2025

Abstract: The project aims to improve diabetes prediction using Artificial Intelligence and Machine Learning (AIML) technologies. Diabetes is a chronic disease that needs to be detected early and monitored regularly. Conventional diagnostic methods are based on clinical evaluation and laboratory tests, which are time-consuming and expensive. The system uses cloud computing and machine learning algorithms to create a scalable and effective diabetes prediction model. With patient health data like glucose levels, BMI, age, and insulin levels, the system implements machine learning techniques like Logistic Regression, Random Forest, and Neural Networks to estimate the probability of diabetes. Integration with the cloud provides real-time analytics, data security, and easy access to healthcare professionals.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://southam.pub/journals/files/shp/shp2025236en.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cwf:shpart:shp2025236

DOI: 10.56294/shp2025236

Access Statistics for this article

More articles in SAP Health and Policy from South American Publishing
Bibliographic data for series maintained by South American Publishing Journals Manager ().