## 3 Results

Table 5: Comparative analysis of medical devices and platforms in remote patient monitoring. * Specificity: Specificity measures the system's ability to identify negative cases correctly. As with sensitivity, system results are compared with known actual values. In this case, the system's power to accurately rule out adverse claims was evaluated, and a specificity value of 0.87 was obtained. * Precision: Precision refers to the proportion of actual positive results about positive results obtained by the system. It is calculated by dividing the real positive cases by the sum of the primary positive points and the false positive claims. In this case, the system's accuracy at detecting certain medical conditions was determined, and a value of 0.91 was obtained. * Accuracy: Accuracy is an overall measure of system performance, calculated by dividing the total number of correct results (positive and negative) by the total number of cases tested. In this case, the ability of the system to correctly classify both positive and negative patients was evaluated, and an accuracy value of 0.89 was obtained. * F-score: The F-score combines precision and sensitivity into a single metric. It is calculated using F = 2
* sensitivity)/(precision + sensitivity). In this case, an F score of 0.91 was obtained, indicating a good balance between precision and sensitivity in detecting certain medical conditions. * Positive Predictive Value: Positive predictive value refers to the proportion of actual positive results relative to all results classified as positive by the system. It is calculated by dividing the real positive cases by the sum of the valid positive points and the false positive claims. On average, the plan was 94.8% accurate for heart rate measurement, 96.2% for temperature measurement, and 94.4% for blood pressure measurement. The results indicate that the system can provide the precise measurements of vital signs in real-time, which may benefit healthcare and clinical decision-making. However, it is essential to note that the results are simulated and may vary in a real scenario, so further testing and evaluation are needed to validate the system's effectiveness. The table presents different evaluation metrics of the cloud data analysis platform implemented by the clinic participating in the study for the IoMT system. The first metric, response time, shows that the system has a fast response, taking only 0.32 s on average to process and analyze data from vital signs monitoring devices. The results in the Table 7 were obtained by collecting data and evaluating the implemented IoMT system. The following details how each of the values was obtained:  
* The frequency of use of the system by patients (per week): To determine the frequency of use, data was collected on the number of times patients used the IoMT system weekly. This information was obtained through system activity logs, which recorded patient interactions with monitoring devices. In this case, it was found that the patients used the system approximately five times per week. \begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Value** \\ \hline Response time (in seconds) & 0.32 \\ Sensitivity & 0.92 \\ specificity & 0.87 \\ Precision & 0.91 \\ Accuracy & 0.89 \\ F-Score & 0.91 \\ Positive predictive value & 0.89 \\ Negative predictive value & 0.92 \\ False positive rate & 0.08 \\ False negative rate & 0.11 \\ Error rate & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Table of model evaluation metrics with a response time value in seconds. * The number of vital signs data collected by the system: The number of essential characters data collected was determined by analyzing the records generated by the monitoring devices. These devices recorded and stored data on patients' vital signs, such as blood pressure, heart rate, and temperature. In this case, approximately 1000 vital signs data were collected.