International Journal of Scientific Research and Engineering Development

Anaemia is a common health condition caused by a deficiency of red blood cells or haemoglobin in the blood. It can lead to fatigue, weakness, and serious health complications if not detected early. Traditional methods of anaemia detection require laboratory tests and manual analysis, which may be time-consuming and inaccessible in rural areas. With the growth of healthcare data and machine learning techniques, intelligent systems can be developed to predict anaemia efficiently. This paper proposes a machine learning–based anaemia prediction system using the Logistic Regression algorithm. The system analyses medical attributes such as haemoglobin level, age, gender, red blood cell count, and other health parameters to predict whether a person is anaemic or non-anaemic. Data preprocessing techniques including data cleaning, normalization, and feature selection are applied to improve model performance. Logistic Regression is chosen because of its simplicity, interpretability, and effectiveness for binary classification problems. Experimental results show that the proposed model provides accurate predictions and can assist healthcare professionals in early diagnosis of anaemia. The system can be integrated into healthcare applications to support quick screening and preventive treatment, especially in rural and lowresource areas.

Vimal R, Dr.K.Banuroopa,"Anemia Prediction Using Machine Learning" International Journal of Scientific Research and Engineering Development, V9(2): Page(372-377) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.