Abstract
Anemia's global grip calls for a groundbreaking solution. We propose a comprehensive machine learning model integrating both traditional (symptoms, history, vitals) and novel data: smartphone-captured conjunctival images. Deep learning extracts hidden visual cues from these images, revealing unseen anemic signatures. This data fusion, unlike analyzing each source alone, creates a multi-faceted risk assessment, boosting accuracy and robustness. But generalizability is key. Rigorous testing across diverse populations ensures real-world effectiveness for everyone. Early detection unlocks the promise of mitigating anemia's impact. Prompt interventions and optimized treatment plans can dramatically improve lives, ushering in a new era of preventive healthcare. This project aligns with global efforts to empower individuals and fight anemia worldwide. Join us as we reshape anemia diagnosis, one image and data point at a time. Keywords : Anemia , Machine Learning, Structured data, Unstructured data, Convolution Neural Network.
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