【摘要】 探索不同剂量紫外线照射后皮肤指标变化趋势，建立晒后皮肤状态评估模型。首先，筛选出变化有规律且具灵敏性的指标，优化黑化模型进一步扩大样本库，利用临床专家对晒后皮肤状态的分级作为学习标准，基于K邻近分类算法（KNN）对指标数据进行训练识别，建立晒后皮肤状态分级评估模型，经10折交叉验证后超参数K=3时，模型的mmce均值为0.015，预测精度acc均值为0.985，预测的准确度高达98.5%。结果表明，该模型能够将晒后皮肤状态的主观评级客观量化，高效率、高精度识别晒后皮肤状态。研究结果可为晒后皮肤状态评估和晒后修护功效评价体系提供技术支持。更多还原

【Abstract】 This study established a model for assessing post-sunburn skin condition by exploring the trends of skin indexes after different doses of UV irradiation. First, we screened out the indicators with regular and sensitive changes,optimized the tanning model to further expand the sample library, used the clinical experts’ grading of post-tanning skin status as the learning standard, and trained the identification of the indicator data based on the K Neighborhood Classification Algorithm(KNN) to establish the post-tanning skin status grading assessment model. After 10-fold cross validation, when the hyperparameter K=3, the mmce mean value of the model is 0.015, and the mean value of the prediction accuracy acc is 0.985, with a prediction accuracy of 98.5%. The results show that the model is able to objectively quantify the subjective ratings of post-tanning skin condition and recognize post-tanning skin condition with high efficiency and accuracy. The results can provide technical support for the assessment of post-sunburn skin condition and the evaluation system of post-sunburn repair efficacy.更多还原