Abstract
ObjectiveChronic 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 and MaterialsIn 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’.The ResultsCP-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.ConclusionCP-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.
Highlights
Chronic Fatigue (CF) is a sub-health status, pathologically characterized by nonspecific extreme fatigue over six months [1]
The Results: Conformal Predictor (CP)-Random Forest (RF) demonstrates an outstanding performance beyond CP-Naıve Bayes classifier (NBC), CP-K Nearest Neighbor (KNN) and ML-KNN under the general metrics of subset accuracy, hamming loss, one-error, coverage, ranking loss and average precision
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
Summary
Chronic Fatigue (CF) is a sub-health status, pathologically characterized by nonspecific extreme fatigue (including physical fatigue and mental fatigue) over six months [1]. Traditional Chinese Medicine (TCM) has provided an effective approach for personalized diagnosis and treatment of CF, and has paid increasing attention as a complementary medicine by the medical researchers [4,5]. TCM diagnosis still causes skepticism and criticism because TCM practitioners diagnose the patient only based on their subjective observation, knowledge, and clinical experience, which lacks objective test and cannot be scientifically proven by clinical trials [6]. It is desired to establish an objective and standardized diagnosis system for CF in TCM. Researchers have found that machine learning technologies are able to figure out the inherent mechanism of TCM diagnosis and provide corrective predictions for patients [7,8]. A computer-aided system aiming at providing objective and reliable diagnosis is highly desired for the better understanding of the TCM diagnosis of chronic fatigue
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