Data Mining for Healthcare Data: A Comparison of Neural Networks Algorithms
Abstract: 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 hosen datasets is Multilayer Perceptron.
It achieves the highest for all performance parameterstested. 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.
Penulis: Debby E. Sondakh
Kode Jurnal: jptkomputerdd170111
