STATISTICAL PREDICTION MODEL FOR ASTHENOPIA

ABSTRACT

Asthenopia is a medical term that covers a range of conditions associated with eye discomfort and fatigue, with Digital Eye Strain (DES) and Computer Vision Syndrome (CVS) being its subcategories. The symptoms of DES closely resemble those of CVS, both of which are linked to the use of digital devices and computers, respectively. In particular, eye strain and discomfort are the common symptoms experienced by the population using digital devices for their day-today activities. Recent research reveals a heightened prevalence of eye conditions in Nigeria, particularly in the aftermath of the Covid-19 pandemic. The increased reliance on digital tools and devices for learning, work, and essential purposes during this period has exposed a significant portion of the population to various eye-related issues. Many individuals may be unaware of these conditions, despite experiencing symptoms that can be linked to them. This study employs machine learning models to forecast the risk level of Asthenopia in users of digital devices. It delves into the critical factors contributing to Digital Eye Strain (DES) while evaluating the performance of Gaussian Mixture and K-Means algorithms. Additionally, the study explores the correlation of variables with the predicted outcomes