Abstract
Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.
| Original language | English |
|---|---|
| Pages (from-to) | 331-338 |
| Number of pages | 8 |
| Journal | Procedia Computer Science |
| Volume | 220 |
| DOIs | |
| Publication status | Published - 2023 |
| Event | 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 - Leuven, Belgium Duration: Mar 15 2023 → Mar 17 2023 |
Keywords
- Artificial Intelligence (AI)
- Cloud Computing
- Diagnosis
- Digital Health
- Edge Computing
- Internet of Things (IoT)
- Logisitic Regression
- Machine Learning
- Prognosis
- Random Forest
- Risk Factors
- Smart Connected Healthcare
- Type 2 Diabetes Mellitus
- eHealth
ASJC Scopus subject areas
- General Computer Science
