optimizing the performance reliability of diagnostic equipment and wearable sensors and medical devices in iomt

Today, healthcare has become an essential part of life, and in the meantime, the internet of things (iot) is widely recognized as a potential solution to reduce the pressure on healthcare systems, which, by its very nature, optimizes the ability the performance reliability of diagnostic equipment, wearable sensors and medical equipment in the internet environment has also been the focus of many recent researches.  therefore, in this research, using neural networks (lstm), an algorithm for optimal diagnosis of medical equipment was proposed and its efficiency was evaluated. the results showed that the lstm architecture together with the dropout layer and the tanh activation function showed better performance and had the lowest average absolute value of error (mape) as well as the root mean square error (rmse) in determining the abnormalities of medical equipment. the accuracy of the proposed method shows 96% and the accuracy, recall and evaluation criteria of the model are 95% respectively. 94.5 and 97% have been calculated, which fully shows the suitability of the proposed algorithm in predicting anomalies and, of course, its suitability for improving the assurance of the proper functioning of medical equipment and sensors.