Noninvasive detection of Vitamin Deficiency using Machine Learning

This paper introduces an accessible and costefficient solution for identifying vitamin deficiencies through the use of AI technology and advanced image processing techniques. By capturing and analyzing photographs of specific body parts such as eyes, lips, tongue, and nails. The application identifies the possible symptoms indicating diseases caused due to nutritional deficiencies and provides high nutritional diet plans. Traditional diagnostic methods are usually invasive and also expensive when it comes to laboratory tests, thus it becomes a disadvantage for general usage. In comparison, our new approach uses non-invasive image analysis to provide the individual with an effective tool for monitoring their nutritional health which is convenient. The system uses machine learning algorithms and data analysis to utilize a large training pattern and characteristics representing different vitamin deficiencies. This empowers individuals to take responsibility of their health while working with healthcare professionals. Medical professionals can feed data and insights into the application to improve its detection accuracy and expand its diagnostic potential. This model ensures continuous improvement and matches with wider public health goals of education and prevention. This paper explains the methodology undertaken for processing image inputs along with integrating machine learning during feature extraction and the consequences of this application. This tool is a significant step towards reducing the commonness and the impact of vitamin deficiencies globally if the health challenges related to nutrition are addressed.