Eye Disease Prediction Among Corporate Employees Using Machine Learning Techniques

EasyChair Preprint no. 10623

Abstract

     The project is entitled as “Eye Disease prediction among Corporate Employees using Machine Learning techniques”. In the IT sector, employees use systems for more than 6 hours, and they may be affected by health problems like: stress, neck pain, back pain etc, because of the nature of work, targets, achievements, night shift, and overwork. Mainly the people use a laptop/desktop for more than 6 hours working continuously to complete their assigned tasks. Therefore, corporate employees may have a chance for eye problems like eye strain, eye pain, burning sensation, double vision, blurring of vision, and frequent watering.

     Computer vision disorder is a major cause nowadays because most people work with computers and spend hours staring at monitors. This type of eye problem is identified as computer vision syndrome (CSV), which includes discomfort in the eye and eye strain. Computer Vision Syndrome is the name given to eye problems caused by prolonged computer use, including eye irritation (dry eyes, itchy eyes, red eyes) and dry vision Headaches.

     The proposed mini-project's main goal is to predict eye diseases, symptoms, and people who are affected in a minor or major way. Machine learning techniques such as Naive Bayes, K- NN algorithm and Support vector machine are implemented for the real time dataset, which are collected from eye hospital’s and employees of the IT industry.

     We have considered 200 records and found 58.2% of the people were affected by eye defects by the most common features of eye diseases have been analyzed according to the performance metrics, such accuracy, precision, sensitivity, F1-Score, etc., to compare the techniques. This project predicted SVM surpassed other models, achieving the greatest accuracy of 98.92% while K-NN achieved 89.25% for the split ratio of 80:20.

Keyphrases: eye disease prediction, KNN, Machine Learning Techniques, Naive Bayes, SVM

@Booklet{EasyChair:10623,
  author = {C Dharani and A Tamilarasi and K Chitra and T Jawahar Karthick and S Jeevitha},
  title = {Eye Disease Prediction Among Corporate Employees Using Machine Learning Techniques},
  howpublished = {EasyChair Preprint no. 10623},

  year = {EasyChair, 2023}}