SILPAKORN UNIVERSITY SCIENCE AND TECHNOLOGY JOURNAL, Vol 7, No 1 (2013)

Principal Component Analysis Coupled with Artificial Neural Networks for Therapeutic Indication Prediction of Thai Herbal Formulae

Lawan Sratthaphut, Samart Jamrus, Suthikarn Woothianusorn, Onoomar Toyama










Abstract


This study illustrated the principal component analysis coupled with artificial neural networks (PC-ANN) as a useful tool in therapeutic indication prediction of Thai herbal formulae official in the National List of Essential Medicine 2011 and the National Traditional Household Remedies. A set of 71 herbal formulae from the National List of Essential Medicine 2011 and the National Traditional Household Remedies and 19 formulae without therapeutic indication was used as a training set, a monitoring set and a validation set. The performance of the model was measured by the percentage of “correctly classified”, True Positive rate and False Positive rate of the PC-ANN model. The results suggested that principal component analysis technique could condense all of the variables in which there were interrelated, into a few principal components, while retaining as much variation presented in the data set as possible. The use of a PC-ANN technique provided a good prediction of therapeutic indication of these herbal formulae as well as distinguishing these formulae from the one without therapeutic indication.

Key Words: Artificial neural network; Principal component analysis; Thai herbal formula

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