NHCE

Permanent URI for this community

Now showing 1 - 1 of 1

Anemia Detection Using Machine Learning
(2024) A Chetu Chandhan - 1NH20AI004; K Lingeswar Balaji - 1NH20AI040; K Datta Ram Vivek - 1NH20AI046; P Sanjay reddy - 1NH20AI143
This study investigates the feasibility of using eye datasets for predictive modeling. Three machine learning algorithms, decision tree, random forest, and XGBoost, were employed to classify individuals with and without anemia. A comprehensive dataset of eye images was obtained and preprocessed to capture key characteristics like color variations, textural patterns, and structural details. These features were then fed into the respective algorithms to construct predictive models. Evaluation metrics, including accuracy, revealed promising performance from all three models in identifying anemia based on eye imagery. Notably, the XGBoost algorithm achieved the highest accuracy, followed by random forest and decision tree. These findings suggest that eye images hold significant potential as a non-invasive and cost-effective tool for early anemia detection. The developed machine learning models utilizing decision tree, random forest, and XGBoost offer a promising avenue for further research and development in this area.