Non-invasive Estimation of Clinical Severity of Anemia Using Hierarchical Ensemble Classifiers

Purpose: Current techniques of anemia classification are either invasive, expensive or inaccurate, making them ill-suited for community health-worker based screening programs. In this study, we propose an Artificial Intelligence (AI) based anemia classification method using a multi-wavelength non-invasive photometry device. Methods: A finger mounted photo-plethysmogram (PPG) device was designed to acquire PPG signals at four wavelengths (590, 660, 810, and 940 nm). A set of 13 attenuation and ratio-of-ratio features, derived using the peak and trough information extracted from the PPG signals, were used to develop a three-way hierarchical ensemble classification scheme using a machine-learning algorithm. PPG data from the device and true hemoglobin data from laboratory-based cell counters was collected for 1583 women of childbearing age and subjects were classified into either healthy (Hemoglobin, Hb > 11 g/dL), anemic (Hb: 7–11 g/dL) or severely anemic (Hb < 7 g/dL) categories. Results: We report a classification sensitivity of 92% (p < 0.05) and specificity of 84% (p < 0.05) in differentiating anemic and non-anemic women. We also report a sensitivity of 76% (p < 0.05), and specificity of 74% (p < 0.05) in identifying severe anemia. Conclusion: We believe that the proposed anemia classification algorithm, along with the associated sensor has the potential to be productized as a low-cost non-invasive anemia-screening device to rapidly determine next steps in clinical decision making in widespread community interventions.