Abstract
Diabetes mellitus is among the most prevalent chronic diseases affecting the world’s population today. With the increasing costs associated with diabetes treatments and management, finding the effective early diabetes detection and
screening tools or methods has become the overarching goal for most contemporary diabetes research. Machine learning methods offer a new approach to diabetes analytics that is well-suited to today’s Big Data requirements. They could overcome many constraints inherent in many traditional statistical modeling approaches. In this paper, we offer concise yet detailed discussions on the current progress in diabetes analytics. We also point to several promising research directions in this area.
screening tools or methods has become the overarching goal for most contemporary diabetes research. Machine learning methods offer a new approach to diabetes analytics that is well-suited to today’s Big Data requirements. They could overcome many constraints inherent in many traditional statistical modeling approaches. In this paper, we offer concise yet detailed discussions on the current progress in diabetes analytics. We also point to several promising research directions in this area.
| Original language | English |
|---|---|
| Pages (from-to) | 32-37 |
| Number of pages | 6 |
| Journal | International Journal of Design, Analysis and Tools for Integrated Circuits and Systems |
| Volume | 6 |
| Issue number | 1 |
| Publication status | Published - Oct 2017 |
| Externally published | Yes |