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. 2021 Oct 6;13(3):8223.
doi: 10.4081/dr.2021.8223. eCollection 2021 Nov 17.

Post-acne hyperpigmentation: Evaluation of risk factors and the use of artificial neural network as a predictive classifier

Firas Al-Qarqaz  1 , Khaldon Bodoor  2 , Ala Baba  1 , Ali Al-Yousef  3 , Jihan Muhaidat  1 , Diala Alshiyab  1
Affiliations

Post-acne hyperpigmentation: Evaluation of risk factors and the use of artificial neural network as a predictive classifier

Firas Al-Qarqaz et al. Dermatol Reports. .

Abstract

Acne is common among young individuals. People with dark skin have a higher risk for developing pigmentary complications. Inflammation is an important factor in post-acne hyperpigmentation however other factors are also involved in developing this complication however these factors are not well studied. The aim of this study is to identify risk factors involved in post-acne hyperpigmentation. Clinical data related to acne, acne- related hyperpigmentation were collected. Data was analyzed for risk factors associated with acne pigmentation. Artificial neural network was used as predictive disease classifier for the outcome of pigmentation. Majority of patients in this study (339 patients) had dark skin phototypes (3 and 4). Post- acne hyperpigmentation was seen in more than 80% of patients. Females, darker skin color, severe acne, facial sites, and excessive sunlight exposure, squeezing or scratching lesions are important risk factors for post-acne hyperpigmentation. Post-acne hyperpigmentation is multifactorial. Several factors implicated in PAH are modifiable by adequate patient education (lesion trauma, excessive sunlight exposure). The use of ANN was helpful in predicting appearance of post-acne hyperpigmentation based on identified risk factors.

Keywords: Acne; Artificial neural network; Classification; Post-acne hyperpigmentation.

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Conflict of interest statement

Conflict of interest: The authors declare no potential conflict of interest.

Figures

Figure 1.
Figure 1.
Site distribution of post-acne pigmentation.
Figure 2.
Figure 2.
Design of an artificial neural network (ANN) for the prediction of post-acne pigmentation. The ANN includes risk factors as the input layer, a hidden layer and the output layer represented by 0 (absence of pigmentation) or 1 (presence of pigmentation).
Figure 3.
Figure 3.
The confusion matrix for the selected features. In the matrix, target class represents observed Pigmented cases and output class represents predicted pigmented cases.
Figure 4.
Figure 4.
The Receiver Operating Characteristic (ROC) curve for confusion matrices.
See this image and copyright information in PMC

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