Advanced machine learning techniques for personalized alopecia areata intervention modeling

Abstract

Alopecia Areata (AA) is a skin condition that causes hair loss, ranging from small patches to complete baldness. Diagnosis and treatment are not very easy because of the complex interaction among genetic, autoimmune, hormonal, environmental, and sociodemographics. In this study, machine learning techniques were explored to assess different stages of Alopecia Areata to allow early diagnosis and treatment. Three complementary datasets consisting of clinical, genetic, environmental, and demographic features were used to build and validate predictive models. Several machine learning models were trained, including Meta Classifiers, K-nearest neigh- bors (KNNs), and Random Forest models. Among all the models tested, the Meta Classifier achieved the highest accuracy and was selected as the base model for fea- ture extraction by LIME and SHAP. Identified key features impacting the models were fed into the fusion datasets for having advanced models such as 1D CNN with autoencoder. The best-performing model was the 1D CNN with an autoencoder, with a 96.78% accuracy, able to integrate the features extracted. This study shows the capability of machine learning to aid early diagnosis and personalized manage- ment of alopecia areata and subsequently provides an avenue for better and more reliable intervention strategies.