Results and Discussion

Table of Contents:

NetSim software is used to simulate the mobile-based IoT environment, which is shown in Figure 1.2. The simulation results are shown in Figures 1.3—1.5.

The transmission link throughput graph is shown in Figure 1.3. The throughput is increasing with respect to time, and after certain time, almost constant values are observed in the graph.

The Erlang call throughput graph is shown in Figure 1.4. It is observed that the throughput is increasing exponentially with respect to time because continuous calls are arrived.

Link 1 throughput graph

FIGURE 1.3 Link 1 throughput graph.

Erlang call throughput graph

FIGURE 1.4 Erlang call throughput graph.

Application metrics

FIGURE 1.5 Application metrics.

From the application metrics table, it is observed that the total number of packet transmitted, the total number of packet received, throughput, and the delay take place in the network.

To validate the proposed model, a real data set [53] is used. The data set is randomly split into two parts. From the data set, 40% of randomly selected data are used to train the model, and remaining 60% of data are used for testing. MatLab R2018a is used for result and simulation.

Figure 1.6 describes the membership grade value of various diseases for different patients. Figure 1.6 segregates different diseases based on their symptoms at the data set. The data set consists of only the value of various symptoms of different patients. After analyzing the data set, total percentage of people suffering from disease or not are determined.

Figure 1.7 is presented as a spiral form. In this figure, the rate of suffered person is shown. Different colors indicate the different disease. Figure 1.7 is plotted based on the training data set.

Based on testing data set, Figure 1.8 is plotted. Sixty percent of data are used for testing purpose. Figure 1.8 shows different people suffering from similar disease, and their symptoms are similar. To determine the optimistic decision of the disease,

Various diseases as per different patients with membership grade

FIGURE 1.6 Various diseases as per different patients with membership grade.

Various symptoms with their threshold value and the number of patients affected

FIGURE 1.7 Various symptoms with their threshold value and the number of patients affected.

expert system is used. The simulation result for optimistic decision for the disease is shown in Figure 1.9. Due to slightly changes of the symptom, a patient is considered for suffering different disease by the medical practitioner.

In this chapter, the maximum value from the collaborative decision is determined. Figure 1.4 describes the maximum value from the collaborative maximize values. After successful testing of the proposed model, the symptoms of ten individual patients are tested. Among the ten patients, six patients are suffering from various

Various symptoms and their corresponding patients

FIGURE 1.8 Various symptoms and their corresponding patients.

Maximum decision value of collaborative optimistic decision

FIGURE 1.9 Maximum decision value of collaborative optimistic decision.

diseases. Patients and their corresponding disease, diagnosed by the proposed model, are shown in Table 1.3.

To measure the efficiency, precision and recall are evaluated and shown in Table 1.4. In this research experiment, the proposed model is tested by three iterations. As per Table 1.4, the overall precision and recall are 89.94% and 88.68%, respectively.

TABLE 1.3

Tested Results from the Proposed Model

SI No

Name of the User

Disease

1

User 1

Fungal infection but in medium stage

2

User 2

Allergy problem

3

User 3

Highly addicted

4

User 4

Severe diabetes

5

User 5

Fungal infection but highly infected

6

User 6

High fever

TABLE 1.4

Performance Analysis of the Proposed Model

Training and Testing Strategy

Strategy 1 (60%-40%)

Strategy 2 (70%-30%)

Strategy 3 (807o-207o)

Precision

89.34%

88.02%

92.46%

Recall

88.16%

86.57%

91.32%

Conclusion

IoT in medical services is tied in with releasing the intensity of associated gadgets and sensors that are generally utilized in the division. IoT can be utilized to get significant experiences from information originating from fetal screens, electrocardiograms, temperature screens, or blood glucose screens. IoT can assume a significant job in human services observation as it would help in early location of medical problems. It would likewise help in incorporating the information gathered from tests in a flash, screen the state of the patient, and afterward hand off that data to the specialists and staff progressively, consequently improving the effectiveness in the general human services framework. In this chapter, an expert system is proposed to determine a maximum optimistic decision from a collaborative decision. The proposed expert system is deployed for medical decisionmaking in a smart city. Statistical approach and fuzzy reasoning methodologies are used to achieve the goal. Deep learning technique would be used to enhance the precision and recall value in the context of big data, which will be considered as future research work.

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