@article{article_1689703, title={Machine Learning Based Investigation of Relation Between Patient and Glabellar Wrinkle Characteristics}, journal={Harran Üniversitesi Tıp Fakültesi Dergisi}, volume={22}, pages={381–387}, year={2025}, DOI={10.35440/hutfd.1689703}, author={Cengizler, Çağlar and Kabakcı, Ayşe Gül and Eren, Dilek and Bozkır, Dursun Murat and Esen, Eda Esra and Türközer, Yaşam and Bozkır, Memduha Gülhal}, keywords={Makine Öğrenmesi, Yapay Zekâ, Cilt Yaşlanması, Demografi}, abstract={Background: The skin around the glabella area is the region most affected by the underlying muscle activity during facial expressions and wrinkling. Although glabellar lines or wrinkles are commonly considered a cosmetic concern in the facial region, objective clinical assessment of this condition remains important. This study investi-gated the relationship between various patient characteristics, potentially reflecting lifestyle and physiological factors, and glabellar wrinkle patterns, which were categorized according to distinct muscle contraction types described in the literature. Materials and Methods: Data were collected from a total of 870 patients. One of the study’s primary objectives was to explore the potential of using multiple patient-related variables in a machine learning-based prediction system while identifying the most influential characteristics associated with glabellar wrinkles. Multiple super-vised machine learning algorithms were employed to uncover potentially nonlinear associations and identify the most informative predictors, including Naïve Bayes, Random Forest, Logistic Regression, and K-Nearest Neigh-bors. Each model was tested across all possible feature subsets to evaluate their predictive performance.
Results: The study aimed to demonstrate that a machine learning-based approach could be functional for the early prediction of glabellar wrinkles. The analysis revealed significant associations between patient characteris-tics, such as age, gender, education, marital status, and occupation, and distinct glabellar wrinkle patterns. Fur-thermore, the machine learning models demonstrated that these characteristics could be used to predict wrin-kle types with considerable performance. In the best scenario, separating the U-type pattern from others, an F1 score of 0.71 was achieved using K-Nearest Neighbors, supporting the potential for early identification and personalized intervention planning.
Conclusions: The study confirmed that specific patient characteristics are strongly related to the formation of different glabellar wrinkle types. In addition, machine learning-based predictive systems showed promising performance, indicating their potential use in supporting clinicians with personalized cosmetic assessments and early intervention strategies.}, number={2}, publisher={Harran Üniversitesi}, organization={Çukurova University Scientific Research Projects Unit}