In the field of optimization, Genetic Algorithm that incorporates the process of evolution plays an important role in finding the best solution to a problem. One of the main issues that arise in the medical field is to search a finite number of factors or features that actually affect or predict the survival of the patients especially with poor prognosis disease, thus helping them in early diagnosis. This paper discusses the various steps that are performed in genetic algorithm and how it is going to help in extracting knowledge out of high dimensional medical dataset. The more the attributes or features, the more difficult it is to correctly predict the class of that sample or instance. This is because of inefficient, useless, noisy attributes in the dataset. So, here the main aim is to search the strong features or genes that can strongly predict the class of subject (patient) i.e. healthy or cancerous and thus help in early detection and treatment.