Predicting Skin Concern Severity From Genetic and Lifestyle Factors: A Comparative Multi-Output Machine Learning Framework

Author First name, Last name, Institution

Yassine Benachour, HCT-Dubai Campuses
Lina Maloukh, Zayed University
Sadok Bouamama, HCT-Dubai Campuses
Barbara Geusens, Nomige

Document Type

Article

Source of Publication

IEEE Access

Publication Date

12-8-2025

Abstract

Personalized dermatology increasingly leverages both genetic predispositions and lifestyle behaviors to model individual skin health outcomes. This study proposes a multi-output machine learning framework to predict the severity of six dermatological phenotypes—acne, redness, dryness, sensitivity, scarring, and pigmentation—using a multimodal dataset of 5,254 individuals. Input features include mutation profiles for six skin-related genes (FLG, MMP1, MMP3, AQP3, SOD2, GPX) and 22 lifestyle variables such as sun exposure, stress, and hydration. We train and evaluate LightGBM models under independent, multi-output, and chained configurations. Performance is assessed using Mean Absolute Error (MAE) and average Quadratic Weighted Kappa (QWK). The proposed ordinal-aware independent LightGBM classifiers achieve superior predictive accuracy (QWKavg ≥ 0.87, MAE ≤ 0.23), providing interpretable insights via gain-based feature importance analysis. Results highlight the differential influence of genetic and lifestyle factors across phenotypes and support the use of explainable AI applications in personalized skincare and dermatological profiling.

DOI Link

10.1109/access.2025.3641629

ISSN

2169-3536

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

13

First Page

207672

Last Page

207693

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

explainable AI, genetic predictors, lifestyle factors, LightGBM, multi-output regression, personalized skincare, precision dermatology, SHapley additive exPlanations, Skin concern severity

Scopus ID

105024602418

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Benachour, Yassine; Maloukh, Lina; Bouamama, Sadok; and Geusens, Barbara, "Predicting Skin Concern Severity From Genetic and Lifestyle Factors: A Comparative Multi-Output Machine Learning Framework" (2025). All Works. 7818.
https://zuscholars.zu.ac.ae/works/7818

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series