An Effective Analysis of Detection of Erythemato Squamous using Machine Learning Algorithms

In the realm of biotechnology, machine learning methods are crucial for illness diagnosis. Expert systems that aid in disease prediction can be created with the knowledge gained via the use of machine learning techniques. This paper discusses various data mining methods for predicting skin diseases. Skin conditions known as erythemato-squamous diseases (ESDs) are widespread. Psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, and pityriasis rubra pilaris are among the six different classifications. With very few exceptions, they all exhibit erythema and scaling as clinical characteristics. The skin illness dataset is also subjected to a feature selection procedure in order to get an accuracy of 99.68% when the Gradient Boosting ensemble method is used on RNC. On the dataset for skin diseases, we found the highest accuracy in the literature. In this paper , we will be discussing about the various machine learning algorithms which shows different classification accuracy with our system.