  Malaria is a deadly disease which claims the lives of hundreds of thousands
of people every year. Computational methods have been proven to be useful in
the medical industry by providing effective means of classification of
diagnostic imaging and disease identification. This paper examines different
machine learning methods in the context of classifying the presence of malaria
in cell images. Numerous machine learning methods can be applied to the same
problem; the question of whether one machine learning method is better suited
to a problem relies heavily on the problem itself and the implementation of a
model. In particular, convolutional neural networks and k nearest neighbours
are both analyzed and contrasted in regards to their application to classifying
the presence of malaria and each models empirical performance. Here, we
implement two models of classification; a convolutional neural network, and the
k nearest neighbours algorithm. These two algorithms are compared based on
validation accuracy. For our implementation, CNN (95%) performed 25% better
than kNN (75%).
