Classification of healthcare data using hybridised fuzzy and convolutional neural network

Abstract

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.

Keywords

• diseases
• support vector machines
• learning (artificial intelligence)
• principal component analysis
• health care
• decision tables
• pattern classification
• medical computing
• fuzzy neural nets
• fuzzy hybridised convolutional neural network model
• probabilistic neural network
• medical data
• healthcare data classification
• decision table
• principal component analysis algorithm
• cardiac disease
• FCNN classifier model
• author classification technique
• machine learning methods
• support vector machine
• decision support pattern