title: Reliable multi-label learning via conformal predictor and random forest for syndrome differentiation of chronic fatigue in traditional Chinese medicine.
abstract: OBJECTIVE
  Chronic Fatigue (CF) still remains unclear about its etiology, pathophysiology, nomenclature and diagnostic criteria in the medical community. Traditional Chinese medicine (TCM) adopts a unique diagnostic method, namely 'bian zheng lun zhi' or syndrome differentiation, to diagnose the CF with a set of syndrome factors, which can be regarded as the Multi-Label Learning (MLL) problem in the machine learning literature. To obtain an effective and reliable diagnostic tool, we use Conformal Predictor (CP), Random Forest (RF) and Problem Transformation method (PT) for the syndrome differentiation of CF.
  
  
  METHODS
  In this work, using PT method, CP-RF is extended to handle MLL problem. CP-RF applies RF to measure the confidence level (p-value) of each label being the true label, and then selects multiple labels whose p-values are larger than the pre-defined significance level as the region prediction. In this paper, we compare the proposed CP-RF with typical CP-NBC(Naïve Bayes Classifier), CP-KNN(K-Nearest Neighbors) and ML-KNN on CF dataset, which consists of 736 cases. Specifically, 95 symptoms are used to identify CF, and four syndrome factors are employed in the syndrome differentiation, including 'spleen deficiency', 'heart deficiency', 'liver stagnation' and 'qi deficiency'.
  
  
  RESULTS
  CP-RF demonstrates an outstanding performance beyond CP-NBC, CP-KNN and ML-KNN under the general metrics of subset accuracy, hamming loss, one-error, coverage, ranking loss and average precision. Furthermore, the performance of CP-RF remains steady at the large scale of confidence levels from 80% to 100%, which indicates its robustness to the threshold determination. In addition, the confidence evaluation provided by CP is valid and well-calibrated.
  
  
  CONCLUSIONS
  CP-RF not only offers outstanding performance but also provides valid confidence evaluation for the CF syndrome differentiation. It would be well applicable to TCM practitioners and facilitate the utilities of objective, effective and reliable computer-based diagnosis tool.
authors.affiliation: College of Computer Science and Technology, Huaqiao University, Xiamen, China.
authors.forename: Huazhen
authors.initials: H
authors.lastname: Wang
authors.affiliation: College of Computer Science and Technology, Huaqiao University, Xiamen, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Liu
authors.affiliation: College of Computer Science and Technology, Huaqiao University, Xiamen, China.
authors.forename: Bing
authors.initials: B
authors.lastname: Lv
authors.affiliation: School of Information Science and Engineering, Xiamen University, Xiamen, China.
authors.forename: Fan
authors.initials: F
authors.lastname: Yang
authors.affiliation: Department of traditional Chinese medicine, Xiamen University, Xiamen, China.
authors.forename: Yanzhu
authors.initials: Y
authors.lastname: Hong
mesh_terms: D001185:Artificial Intelligence; D015673:Fatigue Syndrome, Chronic / Q000145:classification / Q000175:diagnosis*; D006321:Heart; D006801:Humans; D008099:Liver; D008516:Medicine, Chinese Traditional; D015233:Models, Statistical; D013154:Spleen