VitaML: Vitamin Deficiency Detection Using Machine Learning
Abstract
Due to a lack of easily accessible and non-invasive diagnostic techniques, vitamin deficiencies are becoming a serious public health concern that frequently go undiagnosed. Conventional testing can be costly, time-consuming, and equipment-intensive. VitaML presents a machine learning-based framework that uses visible biomarkers, skin characteristics, and discoloration to detect vitamin deficiencies. VitaML seeks to develop a rapid, inexpensive, and user-friendly diagnostic tool by training classification models on a dataset of visual cues associated with particular deficiencies. This system can encourage preventive care, increase awareness of general nutritional health, and assist medical professionals in identifying problems early.
Fields of Study & Subjects
Artificial Intelligence in Healthcare
Industrial Vision Systems and Defect Detection
Smart Agriculture and AI
Publication details
- Journal / source
- International Research Journal on Advanced Engineering and Management (IRJAEM)
- Volume
- 4
- Issue
- 03
- Pages
- 295–300
- Publication date
- 5 March 2026
- Language
- EN