SKIN DISEASES PREDICTION USING MACHINE LEARNING ALGORITHM

Abstract

Skin illnesses are a serious and concerning issue in society owing to the physical 
and psychological consequences they have on individuals. Detecting skin illnesses at an early 
stage is critical for therapy. The procedure of identifying and treating skin damage is dependent 
on the expert doctor's ability and experience. The diagnostic procedure must be precise and 
timely. 
Recently, artificial intelligence research has been employed in the area of skin disease 
diagnosis, using machine learning algorithms and the large quantity of data accessible in health 
centers and hospitals. Many prior works on techniques of categorization of skin disorders based 
on the idea of machine learning were included in this publication. The researchers employed 
several systems, processes, and algorithms in earlier investigations. Several approaches have 
been developed that have been effective in identifying skin disorders and reaching varied 
degrees of diagnostic accuracy. Several systems have relied on image processing and feature 
extraction approaches to forecast and identify illness kind. Other approaches are meant to 
diagnose certain forms of skin disease using clinical symptoms and tissue analyses acquired 
after a skin biopsy of the afflicted region. According to the results of this study, the diagnostic 
accuracy of image processing techniques was very variable, ranging from 50% to 100%. 
The approaches for processing tissue characteristics have a good degree of accuracy of 94% or 
above. The findings offer an overview of the actual relevant studies discovered in the literature 
and indicate the majority of the research gaps that have appeared.

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