  Today, more than 12 million people over the age of 40 suffer from ocular
diseases. Most commonly, older patients are susceptible to age related macular
degeneration, an eye disease that causes blurring of the central vision due to
the deterioration of the retina. The former can only be detected through
complex and expensive imaging software, markedly a visual field test; this
leaves a significant population with untreated eye disease and holds them at
risk for complete vision loss. The use of machine learning algorithms has been
proposed for treating eye disease. However, the development of these models is
limited by a lack of understanding regarding appropriate model and training
parameters to maximize model performance. In our study, we address these points
by generating 6 models, each with a learning rate of 1 * 10^n where n is 0, -1,
-2, ... -6, and calculated a f1 score for each of the models. Our analysis
shows that sample imbalance is a key challenge in training of machine learning
models and can result in deceptive improvements in training cost which does not
translate to true improvements in model predictive performance. Considering the
wide ranging impact of the disease and its adverse effects, we developed a
machine learning algorithm to treat the same. We trained our model on varying
eye disease datasets consisting of over 5000 patients, and the pictures of
their infected eyes. In the future, we hope this model is used extensively,
especially in areas that are under-resourced, to better diagnose eye disease
and improve well being for humanity.
