Open AccessData Mining for Healthcare Data: A Comparison of Neural Networks Algorithms
Author(s) -
Debby Erce Sondakh
Publication year - 2017
Publication title -
cogito smart journal
Language(s) - English
Resource type - Journals
eISSN - 2541-2221
pISSN - 2477-8079
DOI - 10.31154/cogito.v3i1.40.10-19
Subject(s) - computer science , multilayer perceptron , perceptron , artificial intelligence , classifier (uml) , artificial neural network , word error rate , machine learning , data mining , mean squared error , pattern recognition (psychology) , algorithm , statistics , mathematics
Classification has been considered as an important tool utilized for the extraction of useful information from healthcare dataset. It may be applied for recognition of disease over symptoms. This paper aims to compare and evaluate different approaches of neural networks classification algorithms for healthcare datasets. The algorithms considered here are Multilayer Perceptron, Radial Basis Function, and Voted Perceptron which are tested based on resulted classifiers accuracy, precision, mean absolute error and root mean squared error rates, and classifier training time. All the algorithms are applied for five multivariate healthcare datasets, Echocardiogram, SPECT Heart, Chronic Kidney Disease, Mammographic Mass, and EEG Eye State datasets. Among the three algorithms, this study concludes the best algorithm for the chosen datasets is Multilayer Perceptron. It achieves the highest for all performance parameters tested. It can produce high accuracy classifier model with low error rate, but suffer in training time especially of large dataset. Voted Perceptron performance is the lowest in all parameters tested. For further research, an investigation may be conducted to analyze whether the number of hidden layer in Multilayer Perceptron’s architecture has a significant impact on the training time.