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Eye diseases are widespread, causing vision problems for millions. Early detection is critical for effective treatment, potentially preventing vision loss (Patibandla, Rao, Murty in Revolutionizing diabetic retinopathy diagnostics and therapy through artificial intelligence: a smart vision initiative. In transformative approaches to patient literacy and healthcare Innovation pp. 136–155. IGI Global, 2024). This study explores using Machine Learning (ML) to predict eye diseases early. Researchers analyze factors like age, gender, and diagnostic keywords to create personalized risk assessments. The study utilizes ML algorithms and statistical techniques, leveraging diagnostic data from both eyes, including keywords for specific conditions like glaucoma and cataracts. This comprehensive approach, incorporating feature engineering, model selection, and cross-validation, aims to build a robust and generalizable model for early eye disease detection. By embracing data-driven insights, (Ji in Journal of Clinical & Translational Ophthalmology 2:47–63, 2024) we empower healthcare professionals to enhance patient outcomes and reduce the burden of eye diseases The study shows promising results for early detection using ML for classifying retinal images. KNN emerged as the most effective algorithm for the specific dataset. Analysis of factors like age and keywords helped identify key predictors of eye disease. This data-driven approach can empower healthcare professionals to improve patient outcomes and potentially reduce the burden of eye diseases. This paper is also comparison between KNN and Random Forest algorithm.

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Early Eye Disorder Detection Using Predictive Modeling and Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Vaishali Ashok Barse,
  • Kavita Suryawanshi

摘要

Eye diseases are widespread, causing vision problems for millions. Early detection is critical for effective treatment, potentially preventing vision loss (Patibandla, Rao, Murty in Revolutionizing diabetic retinopathy diagnostics and therapy through artificial intelligence: a smart vision initiative. In transformative approaches to patient literacy and healthcare Innovation pp. 136–155. IGI Global, 2024). This study explores using Machine Learning (ML) to predict eye diseases early. Researchers analyze factors like age, gender, and diagnostic keywords to create personalized risk assessments. The study utilizes ML algorithms and statistical techniques, leveraging diagnostic data from both eyes, including keywords for specific conditions like glaucoma and cataracts. This comprehensive approach, incorporating feature engineering, model selection, and cross-validation, aims to build a robust and generalizable model for early eye disease detection. By embracing data-driven insights, (Ji in Journal of Clinical & Translational Ophthalmology 2:47–63, 2024) we empower healthcare professionals to enhance patient outcomes and reduce the burden of eye diseases The study shows promising results for early detection using ML for classifying retinal images. KNN emerged as the most effective algorithm for the specific dataset. Analysis of factors like age and keywords helped identify key predictors of eye disease. This data-driven approach can empower healthcare professionals to improve patient outcomes and potentially reduce the burden of eye diseases. This paper is also comparison between KNN and Random Forest algorithm.