Erythemato-Squamous Diseases Prediction and Interpretation Using Explainable AI
Abhishek Singh Rathore, Siddhartha Kumar Arjaria, Manish Gupta, Gyanendra Chaubey, Amit Kumar Mishra, Vikram Rajpoot
Abstract
Erythemato-squamous diseases (ESD) diagnosis is a significant challenge in dermatology. It is divided into six categories. Artificial intelligence models have been applied to categorize these categories. Artificial intelligent models are black boxes in nature. The objective of this study is to unbox the black-box behavior and interpret the decision-making. Random Forest and XGBoost models are applied on a standard dataset with SHAP value to get interpretability and causability of decision. The Random Forest model had a classification accuracy of 98.21%. Integration of explainability increase the transparency of result and identify the root cause of the disease in the subject. A comprehensive quantitative study will help to adopt artificial intelligence in healthcare with ethical issues like transparency, causability, and interpretability of diagnosis.