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An integrated approach to performance measurement, analysis, improvements and knowledge management in healthcare sector

An integrated approach to performance measurement, analysis, improvements and knowledge... In this proposed work, the knowledge management process adopted for analysing healthcare data used to making the particular decision in care services. In this paper, a fuzzy hybridised convolutional neural network (FCNN) model is stated to guess the class of healthcare data. This model collects the knowledgeable information from the dataset and builds the decision table based on the collected features from datasets. The attributes that are unrelated are deleted by using PCA algorithm. The performance of our classification technique is measured with various metrics such as accuracy, F-measure, G-mean, precision, and recall. The experimental results while compared with some of the existing machine learning methods like probabilistic neural network, support vector machine and neural network, shows the higher performance of FCNN. This model presented in this paper act as a decision support pattern and knowledge management in healthcare for therapeutic specialists. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Knowledge Management Studies Inderscience Publishers

An integrated approach to performance measurement, analysis, improvements and knowledge management in healthcare sector

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1743-8268
eISSN
1743-8276
DOI
10.1504/IJKMS.2019.097130
Publisher site
See Article on Publisher Site

Abstract

In this proposed work, the knowledge management process adopted for analysing healthcare data used to making the particular decision in care services. In this paper, a fuzzy hybridised convolutional neural network (FCNN) model is stated to guess the class of healthcare data. This model collects the knowledgeable information from the dataset and builds the decision table based on the collected features from datasets. The attributes that are unrelated are deleted by using PCA algorithm. The performance of our classification technique is measured with various metrics such as accuracy, F-measure, G-mean, precision, and recall. The experimental results while compared with some of the existing machine learning methods like probabilistic neural network, support vector machine and neural network, shows the higher performance of FCNN. This model presented in this paper act as a decision support pattern and knowledge management in healthcare for therapeutic specialists.

Journal

International Journal of Knowledge Management StudiesInderscience Publishers

Published: Jan 1, 2019

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