Predictive Segment Analysis in Atopic Eczema Using Machine Learning Algorithm

Abstract

Atopic eczema (AE) is a skin disorder that results in chronic inflammation and itch. Predicting atopic eczema patient segments likely to suffer from severe occurrences is important in early intervention as well as personalized therapy. The work here looks at the implementation of Convolutional Neural Networks (CNN) and Exploratory Data Analysis (EDA) methodologies to assess and forecast atopic eczema patient segments likely to exhibit high-risk occurrence. Utilizing a dataset of clinical, environmental, and genetic variables, this work seeks to improve the precision of AE risk stratification. Most notable findings show that CNNs are able to successfully extract spatial patterns from radiological images, while EDA methods assist in determining important trends and correlations within patient data. The suggested methodology shows remarkable enhancement in predictive capability over conventional statistical techniques, laying the groundwork for the incorporation of deep learning and data-driven results into clinical decision-making.