Non-invasive Anemia Detection and Prediagnosis

Background: Anemia is a significant global health concern, often stemming from iron deficiency or deficiencies in folate, vitamins B12, and A. Anemia disproportionately impacts vulnerable populations like children, adolescent girls, and pregnant or postpartum women. Purpose: Anemia is a serious public health issue, impairing productivity, cognitive development, and increasing mortality rates. Anemia is usually detected through blood tests measuring hemoglobin levels, but non-invasive solutions are rquired to lower discomfort, enhance accessibility, and allow for regular monitoring. These methods are essential for early detection in vulnerable populations. Methodology: The research methodology involves extracting valuable information from nail images using data mining algorithms. The focus is on calculating the percentage of blue- and red-stained cells within specific regions of interest in the nail images. Machine-learning algorithms are employed to transform these data into actionable insights for disease diagnosis. Results: The system demonstrates effectiveness in accurately detecting anemia and providing prediagnosis reports to healthcare providers. The reports include comprehensive information such as patient symptoms, health history, test results, and the doctor’s preliminary assessment. This aids in timely and accurate treatment decisions. Conclusion: This research showcases the potential of image processing and machine learning in improving anemia diagnosis and facilitating personalized healthcare interventions.