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

• Published in: Journal of medical and biological engineering 2022-12, Vol.42 (6), p.828-838
• Main Authors: Chakraborty, Sushovan, Kansara, Krity, Dinesh Kumar, R, Swaminathan, Dhivya, Aatre, Kiran, Acharya, Soumyadipta
• Format: Article
• Language: English
• Subjects: Algorithms; Anemia; Artificial intelligence; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biomedicine; Classification; Decision making; Feature extraction; Hemoglobin; Machine learning; Original Article; Photometry; Regenerative Medicine/Tissue Engineering; Screening; Sensitivity
• ISSN: 1609-0985; 2199-4757
• DOI: 10.1007/s40846-022-00750-3

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