Research Article
An IoMT-Enabled Smart Healthcare Model to Monitor Elderly People Using Machine Learning Technique
Table 4
Effectiveness comparison of precision, recall, fscore, and accuracy of different ML-based IoMT-enabled SHC models.
| | Accuracy | Sensitivity (TPR) | Specificity (TNR) | Miss rate (FNR) | Fallout (FPR) | LR+ | LR− | PPV (precision) | NPV |
| ANN | 0.936 | 0.934 | 0.937 | 0.064 | 0.062 | 15.064 | 0.068 | 0.924 | 0.946 | SVM | 0.89233 | 0.89213 | 0.90113 | 0.08523 | 0.07423 | 9.3443 | 0.09143 | 0.65432 | 0.90501 | KNN | 0.84828 | 0.84765 | 0.8513 | 0.08012 | 0.06923 | 8.2311 | 0.87213 | 0.84612 | 0.84621 | Decision tree | 0.83886 | 0.83776 | 0.8432 | 0.07933 | 0.06831 | 8.1432 | 0.85231 | 0.83531 | 0.83632 |
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