Online Detection of Malnutrition Induced Anemia from Nail Color using Machine Learning Algorithms

This chapter enlightens the identification of anaemia due to malnutrition from the colour of the nail images using a smartphone application. This method enables remote measurements and monitoring using a noninvasive procedure. Since this method does not involve invasive techniques, there is no blood loss, and it is painless. In addition, the smartphone application facilitates easy measurements of various physiological parameters related to the blood. They include Hemoglobin (Hb), iron, folic acid, and Vitamin B12. This technique can be accomplished using a feed-forward neural network trained with a Radial Basis Function Network (R.B.F.N.). The image of the fingernails is photographed using a camera built into the smartphone. Online anaemia detection smartphone application will classify the anaemic and Vitamin B12 deficiencies as onset, medieval, and chronic stages by feature extraction from the nail images. The specific measurements made instantly can extract features like the colour and shape of the fingernails. These features train the R.B.F.N. to identify Anemia due to malnutrition. This method will enable the depreciation and disposal problems associated with bio-medical waste. Also, this method will offer a contactless online measurement scheme. The application could help in the early detection of Anemia due to malnutrition, allowing users to seek medical advice and intervention promptly. In terms of accessibility, by utilizing a smartphone application, this technology could reach a broad audience, including those in remote or underserved areas. ;Regarding the privacy of medical images, Blockchain's encryption and decentralization would enhance data privacy and control for users. The data extracted from the nail images for research is obtained with the user's consent. Anonymized data could be used for research purposes, contributing to a better understanding of anaemia and malnutrition trends.