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Modeling of diagnosis for metabolic syndrome by integrating symptoms into physiochemical indexes.
CONCLUSION: Compared with SVM and DT models, the RF model showed the best performance, especially when the ratio of the training set to test set is 8:2. Compared with single predictive indexes, the model constructed by combining physiochemical indexes with TCM indexes (i.e. the fused indexes) exhibited better predictive ability. In addition to common physicochemical indexes, some TCM indexes, such as wiry pulse, chest tightness, spontaneous perspiration, greasy tongue coating, can also improve diagnosis of MS. PMID: 33588265 [PubMed - as supplied by publisher]
Source: Biomedicine and pharmacotherapy = Biomedecine and pharmacotherapie - February 12, 2021 Category: Drugs & Pharmacology Authors: Xia SJ, Gao BZ, Wang SH, Guttery DS, Li CD, Zhang YD Tags: Biomed Pharmacother Source Type: research

Modeling of diagnosis for metabolic syndrome by integrating symptoms into physiochemical indexes
CONCLUSION: Compared with SVM and DT models, the RF model showed the best performance, especially when the ratio of the training set to test set is 8:2. Compared with single predictive indexes, the model constructed by combining physiochemical indexes with TCM indexes (i.e. the fused indexes) exhibited better predictive ability. In addition to common physicochemical indexes, some TCM indexes, such as wiry pulse, chest tightness, spontaneous perspiration, greasy tongue coating, can also improve diagnosis of MS.PMID:33588265 | DOI:10.1016/j.biopha.2021.111367
Source: Biomedicine and pharmacotherapy = Biomedecine and pharmacotherapie - February 15, 2021 Category: Drugs & Pharmacology Authors: Shu-Jie Xia Bi-Zhen Gao Shui-Hua Wang David S Guttery Can-Dong Li Yu-Dong Zhang Source Type: research

CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies
ConclusionsDL-based, comprehensive SSAT, DSAT, and VAT analysis tool showed high accuracy and reproducibility and provided a comprehensive fat compartment composition analysis and visualization in less than 10  s.
Source: Magnetic Resonance Materials in Physics, Biology and Medicine - August 2, 2021 Category: Materials Science Source Type: research