• Version
• Download 29
• File Size 181.48 KB
• File Count 1
• Create Date 21 March 2025
• Last Updated 21 March 2025

Anemia Prediction Using Machine Learning: A Comprehensive Review

 

 

 

Authors:

Mr.Suresh S

Asst.Prof.,Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore

Email - sureshs@skasc.ac.in

 

Hashini S  (22BCS164)

UG Student, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore

Email - Hashinisankrish@gmail.com

Abstract

Anemia, a condition characterized by a deficiency of hemoglobin or red blood cells, is a condition that occurs in a high percentage of individuals globally, particularly in developing and low-income nations. Successful treatment depends on early detection and timely intervention. Since conventional diagnostic methods are often expensive and time-consuming, it may be difficult for individuals in circumstances with limited resources to access the treatment they require. With the promise of creating accurate and cost-effective substitutes, machine learning (ML) offers hopeful strategies to enhance anemia diagnosis and prediction. This paper investigates the benefits, applications, challenges, and possibilities of machine learning (ML) in medicine and the numerous ways that are used in anemia prediction. The prospective applications of machine learning in the improvement of the diagnosis of anemia are also emphasized in the study.

Keywords

Iron deficiency, data analytics, clinical decision support systems, iron deficiency, early detection, diagnostic systems, artificial intelligence, machine learning, predictive modeling, and healthcare.

Download