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. 2019 Feb 12;7(1):e11728. doi: 10.2196/11728

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©Liyan Pan, Guangjian Liu, Xiaojian Mao, Huixian Li, Jiexin Zhang, Huiying Liang, Xiuzhen Li. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 12.02.2019.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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Results of LIME with XGBoost and Random Forest classifiers applied to one positive (A, B) and one negative (C, D) instance. The left sides are for XGBoost, and the right for Random Forest. Blue color is for the negative instance and orange is for the positive instance. The first column represents the prediction probabilities of negative and positive results achieved from classifiers. The second column shows the features’ contributions to the probability. Only the top nine features are displayed for clarity. The third column displays the original data values. LIME: local interpretable model-agnostic explanations; XGBoost: extreme gradient boosting; LH: luteinizing hormone; IGF-I: insulin-like growth factor-I; FSH: follicle-stimulation hormone; PRL: prolactin; GH: growth hormone; E2: estradiol; BMI: body mass index; TTE: testosterone; Rbreast: right breast; Lbreast: left breast; IGFBP-3: insulin-like growth factor binding protein-3; PMT: pigmentation; MST: menstruation; PMT: pigmentation.