Abstract: Medical era has developed with a terrible level of achievements in disease pattern prediction, prevention and cure with the advancements of data mining techniques. Among various mining techniques feature selection occupies an indispensable role for the sake of improving accuracy in any kind of forecasting or cure of diseases. Thus it is treated as an essential earlier work of any kind of mining techniques. Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease status, and how to determine the final gene set that is best for classification. Associations among genetic markers mean one can exploit information redundancy to potentially reduce classification cost in terms of time and computation cost. So in this project, we can implement the framework to predict the diseases using optimization and classification algorithm such as Genetic algorithm and Semi-supervised deep learning algorithm with improved accuracy rate. This paper presents an overview of various disease classification methods and evaluates these proposed methods based on their classification accuracy, computational time and ability to reveal gene information. We have also evaluated and introduced various proposed gene selection method.

Keywords: Microarray data, Bio-markers. High Dimension data, Optimization, Deep learning algorithm

PDF | DOI: 10.17148/IJARCCE.2021.10474

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