Non-Invasive Estimation of Clinical Severity of Anemia using Hierarchical Ensemble Classifiers

Abstract Current techniques of anemia classification are either invasive or inaccurate, making them ill-suited for community-based screening programs. We propose an Artificial Intelligence (AI) based anemia classification method using a multi wavelength non-invasive photometry device. 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 features extracted from the PPG signals were used to develop a three-way classification scheme using a machine-learning. In a study conducted on 1583 women of childbearing age, subjects were classified into either healthy (Hemoglobin, Hb >11 g/dL), anemic (Hb: 7-11 g/dl) or severely anemic (Hb <7g/dL). We report 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. The proposed anemia classification algorithm, along with the associated sensor has the potential to be productized as a low-cost non-invasive rapid anemia screening device for community interventions.