Open AccessClassification of healthcare data using hybridised fuzzy and convolutional neural network
Author(s) -
Ramasamy Balamurugan,
Hameed Abdul Zubar
Publication year - 2019
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2018.5046
Subject(s) - computer science , artificial intelligence , convolutional neural network , artificial neural network , machine learning , support vector machine , data mining , classifier (uml) , data modeling , data set , precision and recall , pattern recognition (psychology) , database
Healthcare performs a key role in the health of humans in the world. While gathering a huge amount of medical data, the problems will appear on the classification of healthcare data. In this work, a fuzzy hybridised convolutional neural network (FCNN) model is stated to guess the class of healthcare data. This model collects the information from the data set and builds the decision table based on the collected features from data sets. The attributes that are unrelated are deleted by using principal component analysis algorithm. The decision of normal and cardiac disease is described by using FCNN classifier. Using the data sets from UCI (University of California Irvine) repository the estimation of the presented model is carried on. The performance of the authors’ classification technique is measured by 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 such as probabilistic neural network, support vector machine and neural network, show the higher performance of FCNN. This model presented in this work acts as a decision support pattern in healthcare for therapeutic specialists.