International Journal of Science, Engineering and TechnologyAuthors: SK.Sharmila, Bandla Manasa, Burugupalli Jahnavi Krishna, Garine Akansha, Gontu Bhavya Reddy
Abstract: Anemia is among the top causes of hematological disorders that have a serious impact on the health of millions of people worldwide and is marked by a shortage of red blood cells or reduction in hemoglobin concentration that leads to oxygen transport to body tissues becoming impaired. Identifying anemia at the earliest stage is extremely important in preventing the development of serious complications, lowering death rate, and enhancing the quality of life of the population especially women and children. Though accurate, the traditional diagnostic methods are quite lengthy, require lots of resources and are difficult to be accessed in places with few resources. This research is about the use of different supervised machine learning algorithms to generate a model that can predict anemia at the initial levels from blood test parameters.Various models such as Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and K-Nearest Neighbors were trained and evaluated with a labeled dataset comprising clinical and blood test features like hemoglobin level, hematocrit, RBC count, and mean corpuscular volume. The dataset was preprocessed to account for missing entries, normalize scales, and optimize feature importance through correlation analysis and recursive feature elimination. Metrics such as accuracy, precision, recall, F1-score, and ROC-AUC were used for comparing the models' performance. Experimental results suggest that ensemble-based algorithms, especially Random Forest, had better predictive accuracy and interpretability. The results indicate that machine learning is a viable tool for healthcare professionals to detect anemia at an early stage, thus allowing for the provision of appropriate treatment and timely intervention. These results pave the way for the seamless incorporation of AI-driven diagnostic tools into everyday healthcare screening routines.
