Accurate Detection and Classification of Alopecia using Machine Learning Models and Interpretation using LIME and SHAP Explainers

Alopecia, a condition marked by partial or complete hair loss, presents diagnostic and treatment difficulties because of its variable manifestations and unclear cause. To understand the complexity of alopecia, this research makes use of the transformational potential of machine learning (ML) and artificial intelligence (AI). This research investigates alopecia incidence prediction models using a variety of machine learning methods, including AdaBoost, Cat Boost, Gradient Boosting, Random Forest, K-Nearest Neighbors, Support Vector Machines, Decision Trees, and XG Boost. The study highlights the significance of accurate prediction models for customized treatment options by incorporating insights from related publications.