A Review on Machine Learning for Nail Diseases Prediction from Ayurvedic Perspective

The work provides an elaboration on the primary method of illness detection within the human body, which involves analyzing nail images of human fingers and extracting data based on nail color. In the Traditional System, practitioners possessed the ability to forecast ailments by examining the condition of an individual’s nails. However, this method was characterized by a prolonged diagnostic process and yielded unsatisfactory outcomes. The purpose of this review is to provide a thorough and inclusive analysis of the present status of nail disease identification through the utilization of machine learning techniques. This paper elucidates the progress, obstacles, and prospective avenues in this pioneering and vital field of medical investigation. The detection of nail diseases has conventionally depended on the manual observations conducted by healthcare practitioners. Recent breakthroughs in the area of machine learning have brought about a significant transformation by offering automated and impartial methods for identifying problems associated to nails. This review paper provides a comprehensive examination of the research and advancements in the identification of disorders of nail through the utilization of machine learning approaches. This study investigates the diverse approaches, datasets, difficulties, and future prospects within the realm of this nascent discipline.