SNP PROFILES FOR PREDICTING THE RISK OF SKIN DISEASES

Abstract

Skin diseases like atopic dermatitis, vitiligo, and psoriasis are considered to be genetically driven by complex genetic variants. This paper outlines a machine learning-based framework for the prediction of skin disease risk with SNP data. According to the dataset, analyzing genotype patterns, the model identifies disease-associated genetic markers and gives an estimation of individual susceptibility. Various algorithms like Random Forest, XGBoost, and more have been trained and tested to determine the most efficient predictor. The approach proposed illustrates the potential of integrating genomics with machine learning to facilitate early risk detection, precision diagnosis, and improved personalized treatment for skin disorders.