Comparison of coverage with different thresholds.

<p>Comparison of coverage with different thresholds.</p

Comparisons of subset accuracy with different <i>K</i> values.

<p>Comparisons of subset accuracy with different <i>K</i> values.</p

Comparison of subset accuracy with different thresholds.

<p>Comparison of subset accuracy with different thresholds.</p

Results of coverage metric with different <i>K</i> values.

<p>Results of coverage metric with different <i>K</i> values.</p

Reliable Multi-Label Learning via Conformal Predictor and Random Forest for Syndrome Differentiation of Chronic Fatigue in Traditional Chinese Medicine

<div><p>Objective</p><p>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.</p><p>Methods and Materials</p><p>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’.</p><p>The Results</p><p>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.</p><p>Conclusion</p><p>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.</p></div

Results of ranking loss metric with different <i>K</i> values.

<p>Results of ranking loss metric with different <i>K</i> values.</p

Comparison of average precision with different thresholds.

<p>Comparison of average precision with different thresholds.</p

Results of one-error metric with different <i>K</i> values.

<p>Results of one-error metric with different <i>K</i> values.</p

Results of average precision metric with different <i>K</i> values.

<p>Results of average precision metric with different <i>K</i> values.</p

Comparison of one-error with different thresholds.

<p>Comparison of one-error with different thresholds.</p