A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach.

The Internet of Medical Things (IoMT) is a huge, exciting new phenomenon that is changing the world of technology and innovating various industries, including healthcare. It has specific applications and changes in the medical world based on what can be done for clinical workflow models. The first and most fundamental thing that IoMT does in healthcare is to bring a flood of new data into medical processes. In this study, an efficient Internet of Medical Things based cancer detection model was proposed. In fact, for many, new fitness monitors and watches are one of the best examples on the Internet; these mobile, portable, wearable devices can record real-time heart rate, blood pressure, and eye movement of cancer patients. These details are sent to doctors or anywhere else. The proposed method leads to a kind of big data renaissance in the health service. The proposed model gets more accuracy while comparing with the existing models. This will help the doctors to analyze the patients' health report and provides better treatment.

1. Introduction

The IoMT brings a flood of new data, but if doctors and others cannot manage it, it can do more harm than good to their workflow process. Without key systems to separate the signal from the noise, IoMT data can overwhelm physicians and disrupt what they are trying to do [1]. When you think about it, a lot of medical work involves diagnosis and accuracy, too much data and not having enough intelligence can be a problem. However, with proper supervision, IoMT can be a real asset, and many are excited about the possibilities [2]. Another way to think about IoMT's results in the medical world is that this new technology creates what has already been established as an excellent best practice; many years ago, the federal HITECH law promoted guidelines for guiding physicians to use electronic medical records. At the time, it was at the forefront of medical technology [3]. But now, IoMT can improve EMR/EHR with better access, as well as better data flows. Notice how real-time heart rate results, and other results may flow into the electronic health record [4, 5]. Consider how the IoMT promotes equal 24/7 access to patient and physician. This is one of the few things that IoMT can do by developing established best practices [6]. Another important way IoMT works in the medical field is to help provide greater flexibility for physicians and other medical professionals [7].

The Internet of Things refers to a network of physical objects that provide the IP address for an Internet connection. The Internet of Things is defined as an invisible and intelligent network that communicates directly or indirectly with each other [8]. The Internet of Things enables communication between physical objects and other systems and devices powered by the Internet. The technological advances in the healthcare sector, government initiatives to expand the Internet of Things, and increased efficiency and cost reduction are key factors driving the Internet of Things market globally [9]. In addition, the Internet of Things makes it much easier and easier for consumers to control electronic devices from one place [10].

An article in Healthcare ID News shows how Citrix is taking doctors' interfaces from a new “fat PC” and creating new encoded scanners in a virtualized environment that can send data to things like wearables and other devices [11-13]. One of the principles here is that doctors can spend less time sitting at a computer screen and sending data to a digital recording system while moving about the office [14]. Another similar idea, wearable, is that it allows provider offices to track patients through operating centers or other offices to see where they are at any stage of the process, by tracking the digital tag attached to the garment [15]. These are just some of the goal setting shareware that you can use. Take a look at some of these trends to remake healthcare in the coming years [16]. The major contribution of this work is to monitor and control the patients' health condition using the Internet of Things (IoT) devices. Here, the smart devices regularly monitor the health condition of the patient and update the information to the doctors. This will be helpful for the doctors to provide the better treatment to the patient.

2. Related Works

Narmatha et al. [17] developed a hybrid fuzzy brain-storm optimization algorithm. This algorithm was designed and classified the MRI scan images based on the brain tumor. Its improved various methods accurately calculated the location and shape of tumors based on brain function and its measurements. Its tumor accuracy is 94.21%.

Elhoseny et al. [18] calculated the number of patients with tumors in the USA-based population. That means that a total of more than 7 lakh people are reported to be living everyday life with different types of tumors, and they estimate that 80% of them are benign malignant tumors and the other 20% are malignant tumors.

El-Dahshan et al. [19] released some data based on current opinion polls. According to the latest estimates, 80,000 people are affected by brain tumors. 55,000 of them are classified as belonging to types 1 and 2. A further 25,000 people are reported to be affected by type 3 and type 4 tumors.

Kong et al. [20] further simplified the computation of tumors. Evolving technologies are increasingly making it easier to calculate and classify tumors. The rise of IoT-based achievements has created a major industrial revolution in this modern age and has made the series of health structures even more special.

Rao et al. [3] reported that computed tomography was performed on different types of brain tumors. They examined and evaluated their series of health conditions. The status of the tumor and the risks associated with its condition can be calculated by these procedures.

Islam et al. [21] designed a model based on simple processes that classify brain tumors. The nature and severity of the disease were diagnosed and analyzed based on the data in its proposed manner. Its improved procedure and computations accurately calculated the classification of tumors.

3. Proposed System

The Medical Internet of Things is one of the rapid growing technologies. In this proposed model, the Internet of things (IoT) devices are used to capture the patients details and undated this into the doctors. It will provide the monitoring details to them, and it is updated in the mobile application; this application details are monitored by the doctors in a remote location. These details are stored in the cloud server which was easily accessed by the hospital patients and doctors. One of these solutions monitors disease dynamics and patient recovery 24/7 with a wearable sensor on the body. Monitoring takes place in real time, starting with the collection of measurements at the hospital and at home and ending with sending the data to the attending physician and laboratory for analysis and decision making. In medicine, programs are used within a hospital to warn staff about the shortage of drugs or equipment. This proposed method is constructed based on the stochastic numerical approach. In it, the numerical data were constructed as per the given input scan images. The patient health condition was analyzed as per the reports. So, these details can be evaluated with the stochastic numerical dataset.

In physical defense, the use of the IoT concept is more attractive than usual. In October 2016, the technology of the Internet of Things was “adopted” by the Ministry of Defense to protect the Crimean naval base; the Ministry of Defense of the Russian Federation purchased the Sentinel-1 security complex. The complex, which includes vibrating bracelets, guarantees the safety of players guarding objects and checking vehicles in “blocks.” Each bracelet is equipped with an “immobility” sensor. When the clock stops moving for more than 30 seconds, the computer sends a vibration signal to his wrist. If the militant “does not survive” within 15 seconds after the alert, an alarm will be declared in the guard room. IoT is a new phase in the development of the Internet, penetrating previously inaccessible areas, bringing about quality change, making people's lives easier, and making companies' jobs more efficient.

We will start the connection by sending a request to accept the device. There are many ways you can send and receive messages in IoT Hub Explorer. A useful option is the simulate-device command. The simulate-device command allows the tool to act as a simulation device command and simulates device reception. It can be used to send user-defined telemetry messages or commands on behalf of the device. Figure 1 shows the IoT-based body tumor monitoring for the proposed methods.

Figure 1

IoT-based body tumor monitoring.

IoMT indication (IoMTI): for every IoT sensor indication, a distinct rate is located to evaluate through the experiential rate.

The convenience of this functionality comes into play when testing development integration on your device, as it reduces the size of the code. You can simultaneously generate messages and track the sending/receiving flow. The command also provides features such as send-interval, send-count, and receive-count to allow configuration of the simulation. It should be noted that this is not a load or penetration test tool and can be used to conduct initial tests prior to in-depth testing. We will send the package of messages to the device we created and then receive a message with the command.where a = the probability constant for all related IoT indication values to the general indication.

After measured the indication values, the detection of tumor is very simple:where b = the sum of all the general indication values.

4. Results and Discussion

In Table 1, it provides the details of probabilities of tumor chances and its percentage ratio. The proposed Internet of Medical Things (IoMTA) was compared with the existing hybrid fuzzy brain-storm optimization algorithm (HFBSO), multifractal texture estimation (MTE), tumor segmentation using chi-square fuzzy C-mean clustering (CSFCC), and hybrid feature extraction method (HFEM). Most of the healthcare industrial segments are predictive because some trial and testing models are available. So, these models are not having any physical outputs until they completed the tests successfully; then, there are enhancements are available. So, the predictive analysis can be used here to get the results.

Table 1

Chances of tumor probabilities.

Chances of tumor probability	Level of result
Above 81%	Tumor confirmation: type 4
60% ≤ probability chance ≥ 80%	Tumor confirmation: type 3
40% ≤ probability chance ≥ 79%	Tumor confirmation: type 2
Below 40%	Tumor confirmation: type 1

There are the 5 parameters to evaluate the water quality, that is, the tumor accuracy, tumor precision, tumor recall, tumor F1-score, and computation time. Before understanding the quality rate of the parameters, the following are analyzed:

Positive-T (TP): it is the perfect predicted correct or above the calibration level.

Negative-T (TN): it is the negative prediction values below the calibration level.

Positives-F (FP): when the exact values are in calibration level and the predicted samples are in same level.

Negative-F (FN): when the exact values are in calibration level but the predicted samples are in different levels.

4.1. Computation of Tumor Accuracy

The tumor accuracy is the parameter which describes the ratio between perfectly predicted tumor input images from the given samples to the total number of collected image samples. When the rate of tumor accuracy is high, then the given output image sample gets the high-quality rate.

Figure 2 shows the various measurement comparison of the tumor accuracy values between the existing HFBSO, MTE, CSFCC, HFEM, and proposed IOMTA.

Figure 2

Measurement of tumor accuracy.

4.2. Computation of Tumor Precision

Tumor precision measurement is the ratio between the positive true samples and total true samples. The total true samples are calculated by the sum of positive true samples and false positive samples.

Figure 3 shows the various measurement comparison of the tumor precision values between the existing HFBSO, MTE, CSFCC, HFEM, and proposed IOMTA.

Figure 3

Measurement of tumor precision.

4.3. Computation of Tumor Recall

Tumor recall measurement is the ratio between the positive true samples and the sum of positive true samples and false negative true samples.

Figure 4 shows the various measurement comparison of the tumor recall values between the existing HFBSO, MTE, CSFCC, HFEM, and proposed IOMTA.

Figure 4

Measurement of tumor recall.

4.4. Computation of Tumor F1-Score

It is measured by the average sample values of tumor precision and tumor recall of the samples.

Figure 5 shows the various measurement comparisons of the tumor F1-score values between the existing HFBSO, MTE, CSFCC, HFEM, and proposed IOMTA.

Figure 5

Measurement of tumor F1-score.

4.5. Computation of Computation Duration

The computation duration is nothing but the time taken to calculate the prediction of two different images.

Figure 6 shows the various measurement comparison of the tumor accuracy values between the existing HFBSO, MTE, CSFCC, HFEM, and proposed IOMTA.

Figure 6

Measurement of computation time (ms).

5. Conclusion

The technological advances in the healthcare sector, government initiatives to expand the Internet of Things, and increased efficiency and cost reduction are key factors driving the Internet of Things market globally. The industry currently has the largest market share in the Internet of Things market, in all application segments. The potential for Internet physics systems to improve productivity in the supply chain and manufacturing process is increasing the demand of the industrial sector in the Internet of Things market. The healthcare and consumer electronics sectors are expected to be the fastest growing applications in the global Internet of Things market. The proposed IoMT algorithm (IoMTA) was getting good accuracy, better precision, great recall rate, fine F1-score, and low computation duration, while compared with the existing hybrid fuzzy brain-storm optimization algorithm (HFBSO), multifractal texture estimation (MTE), tumor segmentation using chi-square fuzzy C-mean clustering (CSFCC), and hybrid feature extraction method (HFEM). In the cutoff range, the proposed method achieves 90% of F1-score, 95% of recall rate, 96% of precision, and 96% of accuracy. Hence, the proposed IoMT algorithm was very accurate to identify the tumors with low time consumption. These computations are very important in the medical field to diagnose the types of tumors in patients. It is especially helpful for physicians to obtain information about the nature of patients and their health from the place where IoMT procedures were performed. The future enhancements of this proposed model are to enhance the treatment and instruction facilities from the doctors while outside the hospital. This remote monitoring module was very much useful to get the treatment suggestions after the patient's discharged from the hospital.