Abstract

Dimensionality reduction techniques have become a vital tool in the investigation of high-dimensional data like gene expression profiles in cancer research. Here is a review, we deliver a comprehensive
overview of dimensionality reduction techniques and their applications in cancer classification. Firstly,
we introduce the concepts and approaches of dimensionality reduction, and after that, we explore
several methods for decreasing dimensionality. These techniques include Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and t-Distributed Stochastic Neighbour Embedding (tSNE). We then present a comprehensive review of applications of these techniques in cancer classification, counting lung cancer, colon cancer, breast cancer, and leukemia. Moreover, we discuss the advantages and disadvantages of different dimensionality reduction techniques in cancer
classification, as well as their limitations and future directions. Finally, we summarize the most recent
stage in the area and make it available for use of some recommendations for future studies. Overall,
this review highlights the importance of dimensionality reduction techniques in classification of cancer and provides a valuable resource for researchers working in this field.
Keywords: Feature extraction, cancer classification, dimensionality reduction, feature selection
INTRODUCTION
A massive portion of digital data has really been continuously produced during the last few years in
a variety of application areas. Moreover, data are becoming exponentially larger, heterogeneous,
complicated, and dimensional. Applications for High Dimensional Data (HDD) have been found in a
variety of industries, such as education, biomedicine, the web, health, social media, and business.

Keyworde: Feature extraction, cancer classification, dimensionality reduction, feature selection

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