Novel neural network model for predicting susceptibility of facial post-inflammatory hyperpigmentation.

BACKGROUND

To construct a neural network model (ATBP) for predicting susceptibility to Post-inflammatory hyperpigmentation (PIH), which is a rapid, objective, and reliable decision-support method before physical and chemical interventions in dermatology clinics for pigment disorders.

MATERIAL AND

METHODS

A dataset was established based on the VISIA Skin Analysis System detection results of 1953 patients with pigment disorders including 93,477 labeled data under 8 indicators. A novel Post-inflammatory hyperpigmentation susceptibility prediction model incorporating Multi-head self-attention mechanism and Back-propagation neural network is proposed to capture the patterns of skin detection data to predict PIH susceptibility.

RESULTS

The results of comparison experiments indicate that Attentive BP (Back Propagation Neural Network) has a significant superiority in prediction accuracy (0.8604) compared with other machine learning models. The ablation experiments prove that the Multi-head self-attention mechanism substantially improves the accuracy and the stability of prediction. The results of the 10-fold cross-validation experiment prove that ATBP is robust and avoids turbulence in predicting.

CONCLUSION

Leveraging Multi-head self-attention mechanism and the architecture advantage of BPNN, the proposed model ATBP obtains the robust and efficient prediction performance in predicting PIH susceptibility via processing large-scale and hi-dimension data, i.e., considering comprehensive skin conditions of individual patient. It can be proved from the experimental results that the proposed model is reliable for decision-support work of PIH susceptibility.