Insights into Ocular Disease Recognition: A Machine Learning Approach

This study reviews the potential benefits of machine learning techniques in diagnosing ocular disorders, aiming to address the limitations of traditional diagnostic methods. Various Machine Learning (ML) algorithms, including Support Vector Machine, Decision Tree, K-Nearest Neighbor, Random Forest, Naïve Bayes, and Extra Trees, are compared to determine the effectiveness of each algorithm in identifying eye diseases. Through analysis and testing, the study concludes that Extra Trees (ET) has achieved the highest accuracy rate of 97%. This finding suggests that ET could be the most suitable option for enhancing the accuracy and efficiency of eye disease detection systems. Overall, these research discoveries contribute to the ongoing efforts to improve diagnostic tools for ocular health, highlighting the potential of Machine Learning (ML) in transforming healthcare.