Novel Ensemble Decision Support and Health Care Monitoring System

Authors

  • Abdelhmid Salih Mohmed Salih Sudan University of Science and Technology Faculty of Computer Science
  • Ajith Abraham Machine Intelligence Research Labs (MIR Labs)

Keywords:

Base Classifiers, Meta base classifiers, Ensemble methods, Voting, wearable sensors

Abstract

In the health care monitoring, data mining is mainly used for classification and predicting the diseases. Various data mining techniques are available for classification and predicting diseases. The aim of this paper is devoted to extensive investigation to construct a new Novel Ensemble Health Care Decision Support for assisting an intelligent health monitoring system and also focus was to reduce the dimensionality of the attributes. Extensive investigate of the experimental results of the performance of different meta
classifiers techniques for classifying the data from different wearable sensors used for monitoring different diseases was carried. Our experiments are conducted on wearable sensors vital signs data set, which was simulated using a hospital environment. First, we carried out a thorough investigation comparing the performance of various base classifiers. Second, we carried out a thorough investigation comparing the performance of various Meta base classifiers. These Meta classifiers used are AdaBoostM1, Bagging, LogitBoost, Random Committee, Stacking, and Voting. Third, we investigated Meta classifiers and new Novel Intelligent Ensemble method was constructed based of Meta classifier Voting combining with three base classifiers J48, Random Forest and Random Tree algorithms. The results obtained show that the Novel Intelligent Ensemble method classifier achieved better outcomes that are significantly better compared with the outcomes of the all Base Classifiers Proposed and all meta base classifiers used in this paper.
Different comparative analysis and evaluation were done using various evaluation methods like Error Metrics, ROC curves, Confusion Matrix, Sensitivity, Specificity and the Cost/Benefit methods. The results obtained show that the Novel Intelligent Ensemble method classifier is very efficient and can achieve high accuracy and, better outcomes that are significantly better compared with the outcomes of the all base classifiers proposed and all meta base classifiers.

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Published

2014-01-01

How to Cite

Abdelhmid Salih Mohmed Salih, & Ajith Abraham. (2014). Novel Ensemble Decision Support and Health Care Monitoring System. Journal of Network and Innovative Computing, 2, 11. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/49

Issue

Vol. 2 (2014)

Section

Original Article

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