Lazy learning refers to machine learning processes in which generalization of the training data is delayed until a query is made to the system. This type of learning is also known as Instance-based Learning. Lazy classifiers are very useful when working with large datasets that have a few attributes. Learning systems have computation occurring at two different times: training time and consultation times. Training time is the time before the consultation time.

The history of healthcare delivery across the globe is more or less the same. While we have seen eradication of some life-threatening diseases, daily medical delivery has seen no significant development, in sync with times. The last transformative change to take place, drastically improving healthcare delivery systems, was with the adoption of computers. Mere incremental changes in a field that needs to be synonymous with transformative ideas, has crippled its pace. At the same time, the world's population is growing at a rapid pace & number of outbreaks and the number of kinds of disease have both increased significantly since 1980, as per a 2014 study published in Journal of Royal Society Interface.