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Disease prediction is of critical importance with respect to advancements in health-care as well as technology. With the advent of big data and analytics, it has become feasible to develop methods to diagnose and predict diseases in cost effective ways. Concepts such as Whole Genome Sequencing has revolutionized healthcare and the way data can be used to tackle diseases. Evolutionary algorithm(EA) or Genetic Algorithm is one such method; it is a subset of evolutionary computation which is a generic population based optimization method. EA is inspired by biological methods such as reproduction, mutation etc. It differs from other optimization methods in its randomness, crossover, mutation and selection. Micro-array data is used extensively for feature subset selection of genes. However, micro-arrays have a large number of features and comparatively small number of samples making disease prediction challenging. Thus, it is important to identify and use the best gene subset selection technique. A comparative approach to the existing methods and its latest versions is studied to analyze the methods that produces the superlative result. This is done with the help of experimental results obtained and its accuracy with respect to disease prediction.

A survey on Genetic Algorithm based Gene Subset Selection for Disease Prediction: Underlying Concepts & Approaches