Prediction of Anemia Disease Using Classification Methods

EasyChair Preprint 3164

Abstract

Sickle cell is hematological disorder (hematological is a study of blood in health and diseases) which may leads Organ damage, heart strokes and serious complications. It may also reduce the human life span. Most of the sickle cells are served in new born Babies It was initially thought to be a particular feature of tribal peoples, but it has now been found in all populations. Sickle cell Symptoms are observed in human beings as episodes of pains (crisis). Painful swelling of hands and feet and Vision problems. By using Artificial Neural Network technique we can identify sickle cells in human body effectively with high accuracy. Detecting sickle cell as early as possible could help the patients to identify their symptoms and can support to take the medications using Antibiotics, Vitamins, Blood transfusion, pain relieving medicines etc .The manual assessment, classification and Counting of cells require for an intense spending of time and it may lead to wrong classification and counting since red blood cells are millions in one smear. The proposed method overcomes these drawbacks by introducing robust and effective classification algorithms to classify the Sickle Cell Anemia in blood cells into three classes namely: Normal (N), Sickle Cells(S) and Thalassemia (T).

Keyphrases: Anemia, Classification, Sickle Cell (SC), Sickle Cell Anemia (SCA), Sickle Cell Disease (SCD), Thalassemia

@booklet{EasyChair:3164,
  author    = {Bathula Pavan Gowtham and Yendluri Hari Chandana and Sagar Yeruva and M. Sharada Varalakshmi and P. E. S. N. Krishna Prasad and Suman Jain and Allam Ravi Kumar Reddy and Saroja Kondaveeti and Padma Gunda},
  title     = {Prediction of Anemia Disease Using Classification Methods},
  howpublished = {EasyChair Preprint 3164},
  year      = {EasyChair, 2020}}