Machine Learning Based Early Cataract Detection: A Predictive Modeling Approach

Year : 2023 | Volume : 01 | Issue : 02 | Page : 1-8

    K. Ramana

  1. A. Venkata Ramana

  2. A. Krishna Mohan

  1. Assistant Professor, Department of Computer Science and Engineering, Rajiv Gandhi University of Knowledge Technologies, Srikakulam, Andhra Pradesh, India
  2. Professor, Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India
  3. Professor, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India


Cataracts, characterized by dense cloudy areas in the eye’s lens, afflict more than 50% of elderly individuals, leading to impaired vision and potential blindness. Detecting cataracts at an early stage is crucial to facilitate simpler treatments, as neglecting the condition may necessitate complex eye surgery. To address this issue, we are creating a predictive system that identifies cataract disease by analyzing user-provided eye features. To achieve this, we leverage OpenCV, a Python library offering a range of image processing and feature extraction capabilities. Our approach incorporates machine learning classification techniques such as support vector machines, random forest, logistic regression, and K-nearest neighbors to construct a robust predictive model. The overarching objective of this project is to develop a dependable and precise tool for the early detection and prediction of cataract disease. This tool holds the potential to enhance patient outcomes and reduce healthcare expenses significantly.

Keywords: Cataract, machine learning, classification, support vector machine (SVM), K-nearest neighbors (KNN), random forest

[This article belongs to International Journal of Computer Science Languages(ijcsl)]

How to cite this article: K. Ramana, A. Venkata Ramana, A. Krishna Mohan Machine Learning Based Early Cataract Detection: A Predictive Modeling Approach ijcsl 2023; 01:1-8
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Regular Issue Subscription Review Article
Volume 01
Issue 02
Received August 28, 2023
Accepted September 22, 2023
Published October 21, 2023