Smart Healthcare Technologies for Massive Internet of Medical Things

Ponnusamy, Vijayakumar; Clement, Joseph; Sriharipriya, K. C.; Natarajan, Sowmya

doi:10.1007/978-3-030-66633-0_4

Cited by 5 publications

(3 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The majority of the medical information provided by IoT devices linked to either a person is essential. A sensor attached to a person, for example, obtains accurate patient records at regular times [ 31 ]. CKD data were translated into an acceptable format in three processes.…”

Section: Proposed Modelmentioning

confidence: 99%

Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases

Rathee¹,

Vinmathi²,

Priya³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

It is essential to change health services from a hospital to a patient-centric platform since medical costs are steadily growing and new illnesses are emerging on a worldwide scale. This study provides an optimal decision support system based on the cloud and Internet of Things (IoT) for identifying Chronic Kidney Disease (CKD) to provide patients with efficient remote healthcare services. To identify the presence of medical data for CKD, the proposed technique uses an algorithm named Improved Simulated Annealing-Root Mean Square -Logistic Regression (ISA-RMS-LR). The four subprocesses that make up the proposed model are a collection of data, preprocessing, feature selection, and classification. The incorporation of Simulated Annealing (SA) during Feature Selection (FS) enhances the ISA-RMS-LR model’s classifier outputs. Using the CKD benchmark dataset, the ISA-RMS-LR model’s efficacy has been verified. According to the experimental findings, the proposed ISA-RMS-LR model effectively classifies patients with CKD, with high sensitivity at 99.46%, accuracy at 99.26%, Specificity at 98%, F-score at 99.63%, and kappa value at 98.29%. The proposed system has many benefits including the fast transmission of medical data to the medical personnel, real-time tracking, and registration condition of the patient through a medical record. Potential enhancement of the performance measures the provider system’s hospital capacity and monitoring of a significant number of patients with a concentrated average delay.

show abstract

Section: Proposed Modelmentioning

confidence: 99%

Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases

Rathee¹,

Vinmathi²,

Priya³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…An expert system is a computer program that uses human knowledge to make a decision, such as a decision made by an expert or an expert. (Ponnusamy et al, 2021) The purpose of an expert system is not to replace the position of an expert or an expert but only to disseminate the knowledge and experience of experts. (Fathushahib & Marselia, 2018) The certainty Factor method is a method of reasoning in expert systems that can overcome uncertainty in disease diagnosis.…”

Section: Kidney Disease Will Readily Occur If the Immunementioning

confidence: 99%

Development of An Expert System for The Diagnosis of Kidney Disease Using the Certainty Factor Method

Maulana

Desiani

2023

bistek

View full text Add to dashboard Cite

Kidney disease is a prevalent health issue affecting millions of people globally. Early and accurate diagnosis of kidney diseases can help in the timely and effective management of the condition. Expert systems, such as those using the Certainty Factor (CF) method, can provide doctors with valuable assistance in diagnosing kidney diseases more efficiently and accurately. This study aims to develop a kidney disease diagnosis expert system using the CF method. The developed system consists of data collection, data storage, and data processing components, with the CF method used to calculate diagnostic confidence levels and decision-making based on predetermined rules. The knowledge acquisition process was carried out by interviewing three nephrologists to obtain rules for diagnosing kidney diseases. The expert system's evaluation is conducted by comparing the system's diagnostic accuracy with a specialist doctors. The results show that the developed expert system has an accuracy rate of 85.7% in diagnosing kidney diseases. The system also has a user-friendly interface, which allows doctors to input symptoms and obtain a diagnosis quickly and accurately. The developed system has several advantages over traditional diagnosis methods. It can diagnose multiple kidney diseases simultaneously and provide a differential diagnosis, allowing doctors to choose the most appropriate treatment plan for their patients. The system also has the potential to reduce diagnostic errors and improve patient outcomes.

show abstract

“…This can aid in monitoring their stress and preventing harmful stress-related disorders. Different ML and deep-learning approaches have been used to detect stress and determine whether a person is stressed [10,11]. An IoMT system can harness the power of ML to improve the detection of health issues by analyzing data collected by sensors [12].…”

Section: Introductionmentioning

confidence: 99%

Priority Detector and Classifier Techniques Based on ML for the IoMT

A. Alsemmeari,

Yehia Dahab,

Alturki

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Emerging telemedicine trends, such as the Internet of Medical Things (IoMT), facilitate regular and efficient interactions between medical devices and computing devices. The importance of IoMT comes from the need to continuously monitor patients' health conditions in real-time during normal daily activities, which is realized with the help of various wearable devices and sensors. One major health problem is workplace stress, which can lead to cardiovascular disease or psychiatric disorders. Therefore, real-time monitoring of employees' stress in the workplace is essential. Stress levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated sooner. However, overwhelming the fog layer with extensive data will increase the load on fog nodes, leading to computational challenges. This study aims to reduce fog computation by proposing machine learning (ML) models with two phases. The first phase of the ML model assesses the priority of the situation based on the stress level. In the second phase, a classifier determines the cause of stress, which was either interruptions or time pressure while completing a task. This approach reduced the computation cost for the fog node, as only high-priority records were transferred to the fog. Low-priority records were forwarded to the cloud. Four ML approaches were compared in terms of accuracy and prediction speed: Knearest neighbors (KNN), a support vector machine (SVM), a bagged tree (BT), and an artificial neural network (ANN). In our experiments, ANN performed best in both phases because it scored an F 1 score of 99.97% and had the highest prediction speed compared with KNN, SVM, and BT.

show abstract

Smart Healthcare Technologies for Massive Internet of Medical Things

Cited by 5 publications

References 66 publications

Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases

Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases

Development of An Expert System for The Diagnosis of Kidney Disease Using the Certainty Factor Method

Priority Detector and Classifier Techniques Based on ML for the IoMT

Contact Info

Product

Resources

About