ABSTRACT
This study investigates the potential of machine learning for diagnosing anemia using eye health data. The researchers implemented Naive Bayes, Random Forest, and Decision Tree algorithms to analyze a dataset containing various eye parameters and corresponding anemia diagnoses. The goal was to identify patterns linking eye conditions to anemia, ultimately aiming to improve early detection and treatment strategies. The analysis revealed promising accuracy in predicting anemia based on ocular features, suggesting that non-invasive eye exams could be a valuable tool for anemia screening. This approach has the potential to streamline diagnosis and expedite intervention for patients.
