IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 537 10.51583/IJLTEMAS.2026.151000018 Machine Learning Ensemble Learning-Based Non-Invasive Anemia Detection Using Conjunctival Image Analysis P V Sona St. Thomas College (Autonomous), Thrissur India Anil George K Dr. St. Thomas College (Autonomous), Thrissur India 15 10 190 200 12 10 2026 17 10 2026 30 10 2026 Anemia detection machine learning ensemble learning stacking conjunctival images Random Forest XGBoost Extra Trees medical image classification Abstract

Anemia is a global health issue characterized by low hemoglobin levels, leading to reduced oxygen transport in the body. Traditional diagnosis relies on invasive blood tests, which can be costly and inaccessible. This study proposes a machine learning-based ensemble framework for non-invasive anemia detection using conjunctival images. Various classifiers, including SVM, Random Forest (RF), XGBoost, and ANN, were trained and evaluated. Ensemble learning techniques such as bagging, boosting, stacking, and voting were applied, with the stacking ensemble (RF + ANN + XGB) achieving the highest accuracy (95.77%). Extra Trees emerged as the best-performing individual model with 96.48% accuracy. The results confirm that ensemble models significantly improve classification performance over individual classifiers. Future work should integrate multi-modal approaches to further enhance diagnostic accuracy. This study highlights the potential of ensemble learning for scalable, non-invasive anemia detection in clinical settings.

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