| Autor: |
AGBOI, J., OKPOR, M. D., EBOKA, A. O., ODIAKAOSE, C. C., EJEH, P. O., AKO, R. E., GETELOMA, V. O., BINITIE, A. P., AFOTANWO, A. |
| Předmět: |
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| Zdroj: |
FUPRE Journal of Scientific & Industrial Research; 2024, Vol. 8 Issue 4, p90-109, 20p |
| Abstrakt: |
Remote health monitor systems have enormous potential of becoming an integral part of medical system. Their outstanding roles are seen in treatment and monitor of patients with critical healthcare issues to reduce unnecessary visits to hospitals and unneeded pressure of healthcare experts. Health care monitors generate enormous data that must be analyzed to aid improved care delivery. We thus, advance a deep learning deep-learning techniques to detect reliability and accuracy of data obtained via an IoT-based remote health monitor. With dataset retrieved from Kaggle, we seek minimum training error that will also result in the best fit, selecting the number of hidden layers (a neuron for each layer) was established via a trail-and-error method, and examining the results. The best possible number of layers was found via tests on single layer with 1-to-20 neurons, and shows that our best F1-score with the least amount of train-loss time is with the configuration of 9-neurons and F1 of 93% and train time loss of 1.140. The accuracy comparison is performed between strongly correlated features and weakly correlated features. Finally, accuracy comparison between two approaches is performed to check which method is performing better for detecting erroneous data for the given dataset. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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