Journal
2021 IEEE 3RD GLOBAL CONFERENCE ON LIFE SCIENCES AND TECHNOLOGIES (IEEE LIFETECH 2021)
Volume -, Issue -, Pages 102-103Publisher
IEEE
DOI: 10.1109/LIFETECH52111.2021.9391949
Keywords
Bacterial pneumonia; Traditional Chinese Medicine; labeling; k-means; random forest
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The study utilizes empirical data from Traditional Chinese Medicine to develop new antibiotics, screening out 2258 potential TCM formulae for treating bacterial pneumonia. Evaluated by the random forest algorithm, the matching labeling performs significantly better than clustering labeling by K-means.
The sizable empirical data from Traditional Chinese Medicine (TCM) is a resource for new antibiotic development. This work screens out 2258 potential formulae for bacterial pneumonia bridging modern medicine diseases and TCM syndromes. Evaluated by the random forest algorithm, the matching labeling performs better with a specificity of 84.34% and a precision of 88.12% compared to the clustering labeling by K-means.
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