Performance Analysis and Feature Extraction for Classifying the Severity of Atopic Dermatitis Diseases

Atopic dermatitis, also known as eczema, is a common chronic skin condition that affects millions of people worldwide. It is characterized by inflamed, itchy, and dry skin, often accompanied by redness, swelling, and the formation of small bumps or blisters. This condition can vary in severity, ranging from mild and intermittent to severe and persistent. Precise and dependable decision-making by a dermatologist is essential to ensure improved therapy and risk stratification for the patient. Usually, dermatologists depend on visual assessment to ascertain a severity score, but this approach is subjective and can vary among different doctors. This research aim to classify the atopic dermatitis severity based on advance machine learning model such as XGBoost, CatBoost, LigthGBM, and Random Forest. Among the evaluated models, the CatBoost model showed as the most effective and reliable in classification, It achieved an accuracy of 89%, precision of 90%, recall of 89%, and an F-Score of 89%.