A Distributed Data Mining System for a Novel Ubiquitous Healthcare Framework

Viswanathan, Murlikrishna; Whangbo, Taeg Keun; Lee, K. J.; Yang, Young-Kyu

doi:10.1007/978-3-540-72588-6_117

Cited by 2 publications

References 5 publications

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A hierarchical, ontology-driven Bayesian concept for ubiquitous medical environments- A case study for pulmonary diseases

Μaragoudakis

Lymberopoulos

Fakotakis

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The present paper extends work on an existing computer-based Decision Support System (DSS) that aims to provide assistance to physicians as regards to pulmonary diseases. The extension deals with allowing for a hierarchical decomposition of the task, at different levels of domain granularity, using a novel approach, i.e. Hierarchical Bayesian Networks. The proposed framework uses data from various networking appliances such as mobile phones and wireless medical sensors to establish a ubiquitous environment for medical treatment of pulmonary diseases. Domain knowledge is encoded at the upper levels of the hierarchy, thus making the process of generalization easier to accomplish. The experimental results were carried out under the Pulmonary Department, University Regional Hospital Patras, Patras, Greece. They have supported our initial beliefs about the ability of Bayesian networks to provide an effective, yet semantically-oriented, means of prognosis and reasoning under conditions of uncertainty.

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A hierarchical, ontology-driven Bayesian concept for ubiquitous medical environments- A case study for pulmonary diseases

Μaragoudakis

Lymberopoulos

Fakotakis

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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A Hybrid Case Based Reasoning Model for Classification in Internet of Things (IoT) Environment

Biswas

Devi

Chakraborty

2018

Journal of Organizational and End User Computing

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This article describes how the enormous size of data in IoT needs efficient data mining model for information extraction, classification and mining hidden patterns from data. CBR is a learning, mining and problem-solving approach which solves a problem by relating past similar solved problems. One issue with CBR is feature weight to measure the similarity among cases to mine similar past cases. NN's pruning is a popular method, which extracts feature weights from a trained neural network without losing much generality of the training set by using four mechanisms: sensitivity, activity, saliency and relevance. However, training NN with imbalanced data leads the classifier to get biased towards the majority class. Therefore, this article proposes a hybrid CBR model with RUS and cost sensitive back propagation neural network in IoT environment to deal with the feature weighting problem in imbalance data. The proposed model is validated with six real-life datasets. The experimental results show that the proposed model is better than other feature weighting methods.

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A Distributed Data Mining System for a Novel Ubiquitous Healthcare Framework

Cited by 2 publications

References 5 publications

A hierarchical, ontology-driven Bayesian concept for ubiquitous medical environments- A case study for pulmonary diseases

A hierarchical, ontology-driven Bayesian concept for ubiquitous medical environments- A case study for pulmonary diseases

A Hybrid Case Based Reasoning Model for Classification in Internet of Things (IoT) Environment

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