PEDL-XAI: A Hybrid Probabilistic Ensemble Deep Learning Approach for Hair Disorder Diagnosis

The study introduces an Explainable Artificial Intelligence (XAI)-based system for diagnosing hair disorders, addressing the limitations of conventional methods that often overlook the complex interplay of factors like genetics and stress. Utilizing the Flask web framework, the system employs various machine learning algorithms and introduces a novel hybrid Probabilistic Ensemble Deep Learning (PEDL) model, which combines a Probabilistic Neural Network with a Sparse Representation Classifier. This model achieves a high accuracy of 0.9950, surpassing traditional approaches. The integration of XAI techniques allows for transparent predictions, identifying key factors, assessing risks, and providing personalized recommendations for hair loss evaluation, treatment, and hormonal impact analysis.