Hash polynomial two factor decision tree using IoT for smart health care scheduling

Ramachandran, Manikandan; Patan, Rizwan; Gandomi, Amir H.; Sivanesan, P.; Kalyanaraman, Hariharan

doi:10.1016/j.eswa.2019.112924

Cited by 46 publications

(12 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A decision tree (DT) is a supervised ML algorithm. Compared to other supervised ML algorithms, the DT is more useful as it can take care of classification and regressions (Manikandan et al 2020). DT provides a tree-like graph that represents an algorithmic structure consisting of conditional control statements.…”

Section: Methodsmentioning

confidence: 99%

A novel enhanced decision tree model for detecting chronic kidney disease

Chaudhuri¹,

Sinha

Banerjee³

et al. 2021

Netw Model Anal Health Inform Bioinforma

View full text Add to dashboard Cite

Prediction of diseases is sensitive as any error can result in the wrong person's treatment or not treating the right patient. Besides, some features distinguish a disease from curable to fatal or curable to chronic disease. Data mining techniques have been widely used in health-related research. The researchers, so far, could attain around 97 percent accuracy using several methods. Some researchers have demonstrated that the selection of correct features increases the prediction accuracy. This research work propose a method to distinguish between chronic and non-chronic kidney disease, identify its crucial features without reducing the accuracy of prediction, and a prediction algorithm to eliminate the possibility of under or overfitting. This study uses the recursive feature elimination (RFE) method that selects an optimal subset of features and an ensemble algorithm, the enhanced decision tree (EDT), to predict the disease. The results obtained in this paper show that the accuracy level of EDT is not changed with the removal of less significant features, thus enabling the decision-makers to concentrate on few features to reduce time and error of treatment. EDT establishes substantially high consistency in predicting, with or without feature selection, the disease.

show abstract

Section: Methodsmentioning

confidence: 99%

A novel enhanced decision tree model for detecting chronic kidney disease

Chaudhuri¹,

Sinha

Banerjee³

et al. 2021

Netw Model Anal Health Inform Bioinforma

View full text Add to dashboard Cite

show abstract

“…is section provides the scheduling details that combine HMFO [26] (Figure 1) with deep layers of neural network (DNN). e weight may influence the task selection process; those who are unable to deal with the problem will quickly be excluded.…”

Section: Hmfomentioning

confidence: 99%

“…IoT jobs are not always tolerant of delays, despite the fact that task scheduling is notoriously difficult. So, the task scheduling architecture should allow IoT applications to be handled quickly while avoiding the complexity of the IoT infrastructure [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Cloud-Internet of Health Things (IOHT) Task Scheduling Using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems

Arivazhagan

Somasundaram²,

Babu

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

Considering task dependencies, the balancing of the Internet of Health Things (IoHT) scheduling is considered important to reduce the make span rate. In this paper, we developed a smart model approach for the best task schedule of Hybrid Moth Flame Optimization (HMFO) for cloud computing integrated in the IoHT environment over e-healthcare systems. The HMFO guarantees uniform resource assignment and enhanced quality of services (QoS). The model is trained with the Google cluster dataset such that it learns the instances of how a job is scheduled in cloud and the trained HMFO model is used to schedule the jobs in real time. The simulation is conducted on a CloudSim environment to test the scheduling efficacy of the model in hybrid cloud environment. The parameters used by this method for the performance assessment include the use of resources, response time, and energy utilization. In terms of response time, average run time, and lower costs, the hybrid HMFO approach has offered increased response rate with reduced cost and run time than other methods.

show abstract

“…Through analysis, they found that the use of telemedicine technology can help children recover from diseases to a certain extent. During the study of the rehabilitation process of patients undergoing coronary artery bypass surgery, the researchers conducted a comparative analysis of whether the patients used online medical health management methods, and found that online medical health management methods can guide the life of patients with high coronary artery bypass surgery [38][39][40].…”

Section: Related Workmentioning

confidence: 99%

Optimization of the Medical Service Consultation System Based on the Artificial Intelligence of the Internet of Things

Mao¹,

Zhang²

2021

IEEE Access

View full text Add to dashboard Cite

Artificial intelligence-assisted diagnosis systems are developing rapidly, but doctors are currently less aware of artificial intelligence-assisted diagnosis systems. Understanding how to allow doctors to accept and use artificial intelligence medical assistant diagnosis system can promote the implementation of artificial intelligence medical assistant diagnosis system applications. Taking into account the current difficulties faced by the Internet of Things medical consultation services, this paper proposes a business operation model based on multi-party participation and sharing of medical consultation resources. We designed the information flow, overall logic and service implementation process of the service model, and completed the construction of the artificial intelligence medical service service model. We combine IoT technology to build a vital signs monitoring environment and clarify how to use IoT devices. In the error backpropagation algorithm, there is no significant difference in the contribution of different samples to the weight change, which makes the adjustment of network parameters not easily affected by difficult medical consultation samples, thereby weakening the effect of network medical consultation. In order to solve this problem, this article defines the degree to which the sample belongs to its correct category as the confidence of medical inquiry, and divides the training sample into a dangerous sample and a safe sample according to a dynamic threshold. Based on the difficulty of medical inquiry, an improved new learning algorithm is proposed. The algorithm penalizes the loss of dangerous samples, so that the convolutional neural network pays more attention to dangerous samples and can learn more effective information. Aiming at the eight physiological characteristics of data, this paper adjusts the structure of the convolutional neural network to make it take into account the richness of data characteristics and the dynamics of data changes over time. The realized CNN optimization algorithm model has improved the prediction effect, and the accuracy rate of medical consultation reaches 90.15%, which is better than other machine learning algorithms.

show abstract

Hash polynomial two factor decision tree using IoT for smart health care scheduling

Cited by 46 publications

References 29 publications

A novel enhanced decision tree model for detecting chronic kidney disease

A novel enhanced decision tree model for detecting chronic kidney disease

Cloud-Internet of Health Things (IOHT) Task Scheduling Using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems

Optimization of the Medical Service Consultation System Based on the Artificial Intelligence of the Internet of Things

Contact Info

Product

Resources

About