A Comparison of Statistical Discriminant Analysis and Artificial Neural Network Model for the prediction of breast cancer

R Jaisankar¹ , D Victorseelan²

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.2 , pp.284-289, Apr-2019

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i2.284289

Online published on Apr 30, 2019

Copyright © R Jaisankar, D Victorseelan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Artificial Neural Network (ANN) is one of the widely used statistical learning techniques in machine learning and cognitive science which is inspired by biological neural networks and basically consists of several non-linear processing units, called neurons or nodes. Though Artificial Neural Network has a wide variety of applications, it can also be used for discrimination of subjects. Statistical Discriminant analysis developed by R.A. Fisher (1936) is still prevailing as a novel methodology for discrimination. This paper presents the results of an experimental comparison of Statistical Discriminant Analysis and Artificial Neural Network (ANN) for predicting the patients affected by breast cancer. Samples of 116 patient’s profiles collected from various private and government hospitals in Coimbatore, India, were used. The power of the model is measured by correct prediction rate. The study reveals that higher accuracy is provided by Neural Network analysis than Discriminant analysis in terms of prediction.

Key-Words / Index Term :
Discriminant Analysis, Machine learning, Artificial Neural Networks, Back propagation algorithm, Training and testing.

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