Table 1 Literature survey of IoT-based healthcare monitoring model.
Author | Methods | Datasets | Platform | Advantages | Limitations |
|---|---|---|---|---|---|
Khan et al.19 | SMSH | Wider-face dataset | MATLAB simulation | Better security with minimum execution time | Lack of adequate medical information |
Manogaran et al.20 | GC-MFR | Cleveland heart disease database | JAVA | Better detection accuracy and minimum task completion time | Data threats with higher cost |
Kaur et al.26 | Random forest (RF) | Public datasets | WEKA open-source tool | 97.26% accuracy and minimum cost | Improper security |
Wu et al.21 | Deep learning | Real-time dataset | MATLAB | Minimum overfitting and enhanced accuracy | Vanishing gradient, higher computational complexity, and cost |
Zhu et al.27 | Edge-fog computing framework | Gliomas datasets | Azure cloud | Smaller latencies with higher accuracy | Does not promote gradual analysis |
Juyal et al.28 | AI-enabled cloud-based IoT | Skin image dataset | MATLAB | Improved accuracy and detection rate | Lots of security threats |