Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

Xie, Yi; Lu, Lin; Gao, Fei; He, Shuangjiang; Zhao, Huapeng; Fāng, Yīng; Yang, Jiaming; An, Ying; Ye, Zhewei; Dong, Zhe

doi:10.1007/s11596-021-2485-0

Cited by 97 publications

(38 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While applying a model of regression analysis, researchers have concentrated more on evaluating numerous linear regression practices for taking suitable decisions in the sustainable future. On the other hand, various pieces of medical evidence also have been gathered for recognizing CT scan images related to lung cancer and noncancer cases [21]. For exploring the accuracy rate in the CNN outcomes, researchers tend to minutely analyze all the factors for regulating the performance of regression analysis toward easy detection in predicting of lung cancer diseases and analyzing food quality.…”

Section: Methodsmentioning

confidence: 99%

An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

Aboamer

Sikkandar

Gupta

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

Deep learning (DL) is a new approach that provides exceptional speed in healthcare activities with greater accuracy. In this regard, “convolutional neural network” or CNN and blockchain are two important parts that together fasten the disease detection procedures securely. CNN can detect and predict diseases like lung cancer and help determine food quality, and blockchain is responsible for data. This research is going to analyze the extension of blockchain with the help of CNN for lung cancer prediction and making food safer. CNN algorithm has been trained with a huge number of images by altering the filters, features, epoch values, padding value, kernel size, and resolution. Subsequently, the CNN accuracy has been measured to understand how these factors affect the accuracy. A linear regression analysis has been carried out in IBM SPSS where the independent variables selected are image dataset augmentation, epochs, features, pixel size (90 × 90 to 512 × 512), kernel size (0–7), filters (10–40), and padding. The dependent variable is the accuracy of CNN. Findings suggested that a larger number of epochs improve the CNN accuracy; however, when more than 12 epochs are considered, the accuracy may decrease. A greater pixel/resolution also improves the accuracy of cancer and food image detection. When images are provided with excellent features and filters, the CNN accuracy improves. The main objective of this research is to comprehend how the independent variables affect the accuracy (dependent), but the reading may not be fully exact, and thus, the researcher has conceded out a minor task, which delivered evidence supportive of the analysis and against the analysis. As a result, it can be determined that image augmentation and a large number of images develop the CNN accuracy in lung cancer prediction and food safety determination when features and filters are applied correctly. A total of 10–12 epochs are desirable for CNN to receive 99% accuracy with 1 padding.

show abstract

Section: Methodsmentioning

confidence: 99%

An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

Aboamer

Sikkandar

Gupta

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

show abstract

“…An IoT is a key concept that focuses on the modeling and connecting of Internet-linked physical objects using the power of computer systems. Instead of using a certain number of high-powered such as computers, tablet devices, mobile phones, and so on [ 1 , 2 ], the IoT is largely used for a range of applications using a large number of low-power devices. Such as wrist bands, refrigerators, and umbrellas.…”

Section: Introductionmentioning

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

“…The 10 selected studies differed in their study design and methods and included research carried out using a quantitative design, 12 – 14 , 17 , 20 – 22 a qualitative design, 10 or mixed-methods approach incorporating both qualitative and quantitative methods. 11 , 18 Overall, selected studies involved 1399 patients with CDs, and the monitoring period lasted from 2 days to 12 months.…”

Section: Resultsmentioning

confidence: 99%

Naturalizing digital and quality of life in chronic diseases: Systematic review to research perspective into technological advancing and personalized medicine

et al. 2022

View full text Add to dashboard Cite

The increasing of chronic diseases and related health management are the main clinical and public health challenges. The long-term nature and the need for continuous monitoring in chronic disease management gave rise to early technological innovations (mobile Health) to improve care management plans, therapeutic adherence, and psychological support to the patient. This review aimed to map the literature on the impact of the use of wearable device on quality of life in patients with chronic diseases. We performed a systematic search of MEDLINE through PubMed, Web of Science, and Scopus of all scientific literature published until January 2022. Based on inclusion and exclusion criteria, a total of 10 papers were included. This review pointed out the relevant focus on the use of wearable device in chronic disease patients highlighting the wearable device impact on several domains including quality of life, Self-Efficacy, Self-Management, and feelings on patients with chronic diseases. The available scientific literature related to the impact of the use of wearable device on quality of life and psychological features in patients with chronic diseases, general underline a need to develop professional healthcare guidelines and tailored intervention on patients with a chronic condition, using mobile Health solutions and trying to fill the lack of knowledge about the topic.

show abstract

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

Cited by 97 publications

References 61 publications

An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases

Naturalizing digital and quality of life in chronic diseases: Systematic review to research perspective into technological advancing and personalized medicine

Contact Info

Product

Resources

About