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(searched for: Classification of Red Blood Cells using Principal Component Analysis Technique)
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Saima Sadiq, Muhammad Usman Khalid, Mui- Zzud- Din, , Waqar Aslam, , , Byung-Won On
IEEE Access, Volume 9, pp 45528-45538; doi:10.1109/access.2021.3066782

Abstract:
Thalassemia is viewed as a prevalent inherited blood disease that has gotten exorbitant consideration in the field of medical research around the world. Inherited diseases have a high risk that children will get these diseases from their parents. If both the parents are β-Thalassemia carriers then there are 25% chances that each child will have β-Thalassemia intermediate or β-Thalassemia major, which in most of its cases leads to death. Prenatal screening after counseling of couples is an effective way to control β-Thalassemia. Generally, identification of the Thalassemia carriers is performed by some quantifiable blood traits determined effectively by high-performance-liquid-chromatography (HPLC) test, which is costly, time-consuming, and requires specialized equipment. However, cost-effective and rapid screening techniques need to be devised for this problem. This study aims to detect β-Thalassemia carriers by evaluating red blood cell indices from the complete-blood-count test. The present study included Punjab Thalassemia Prevention Project Lab Reports dataset. The proposed SGR-VC is an ensemble of three machine learning algorithms: Support Vector Machine, Gradient Boosting Machine, and Random Forest. Comparative analysis proved that the proposed ensemble model using all indices of red blood cells is very effective in β-Thalassemia carrier screening with 93% accuracy.
, S. Phani Kumar
Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making pp 759-780; doi:10.1007/978-981-15-1097-7_64

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European Journal of Engineering and Technology Research, Volume 4, pp 17-22; doi:10.24018/ejers.2019.4.2.1007

Abstract:
Principal component analysis (PCA) is based feature reduction that reduces the correlation of features. In this research, a novel approach is proposed by applying the PCA technique on various morphologies of red blood cells (RBCs). According to hematologists, this method successfully classified 40 different types of abnormal RBCs. The classification of RBCs into various distinct subtypes using three machine learning algorithms is important in clinical and laboratory tests for detecting blood diseases. The most common abnormal RBCs are considered as anemic. The RBC features are sufficient to identify the type of anemia and the disease that caused it. Therefore, we found that several features extracted from RBCs in the blood smear images are not significant for classification when observed independently but are significant when combined with other features. The number of feature vectors is reduced from 271 to 8 as time resuming in training and accuracy percentage increased to 98%.
Journal of Biomedical Optics, Volume 20; doi:10.1117/1.jbo.20.1.016005

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Steve Selvin
Epidemiologic Analysis pp 93-107; doi:10.1093/acprof:oso/9780195146189.003.0007

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