IMPROVING EYE CARE WITH PREDICTIVE MODELING: MACHINE LEARNING APPROACH TO CONTACT LENS FITTING

Abstract

The fitting of Contact Lens is a complex process that traditionally relies on the Clinicans Expertise and
patient feedback. Incorrect fitting can lead to discomfort vision problems and eye health issues.
However, the variability in individual eye characteristics and patient responses often results in trial-anderror approach leading based on decision support system for contact lens fitting. Leverage predictive
modelling to enhance eye care. The system aims to assist eye care professionals in informed decision,
reducing errors and improving patient outcomes. By leveraging extensive datasets containing patient
demographic, ocular measurements and historical fitting outcomes, the system predicts the most
suitable contact lens for each patient. The machine learning model is Support Vector Classifier (SVC)
where this model trained and validated to enhance the accuracy and reliability of the fitting process.
The results of this SVC model demonstrate a significant improvement in fit accuracy, reduced fitting
time and increases the patient satisfaction compared to traditional methods. The implementation of this
technology not only simplified the contact lens fitting process but also represents a crucial step towards
personalized eye care, offering practitioners as a powerful tool to enhance decision-making and satisfies
the patient outcomes.