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Principal Component Analysis Coupled with Artificial Neural Networks for Therapeutic Indication Prediction of Thai Herbal Formulae |
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| รหัสดีโอไอ | |
| Creator | 1. Lawan Sratthaphut 2. Samart Jamrus 3. Suthikarn Woothianusorn 4. Onoomar Toyama |
| Title | Principal Component Analysis Coupled with Artificial Neural Networks for Therapeutic Indication Prediction of Thai Herbal Formulae |
| Publisher | Silpakorn University Research and Development Institute |
| Publication Year | 2556 |
| Journal Title | Silpakorn University Science and Technology Journal |
| Journal Vol. | 7 |
| Journal No. | 1 |
| Page no. | 41-48 |
| Keyword | Artificial neural network, Principal component analysis, Thai herbal formula |
| ISSN | 1905-9159 |
| Abstract | This study illustrated the principal component analysis coupled with artificial neural networks (PCANN) 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 therapeuti indication. |
