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Machine learning is a part of artificial intelligence and its optimization techniques are allows system to learn from past example and identify pattern from large dataset. This criteria are well suited for researchers for doing their research on medical application, genomic measurements and it cause to machine learning techniques are used in cancer diagnosis and detection. Recent year machine learning has been applied to cancer prediction and prognosis. This latter approach growing trend towards an artificial neural network. ANN ensemble is a learning platform in which a number of neurons work together to solve a problem. Our approach is used to identify adenocarcinoma cancer diseases from gene expression data samples obtained from bodies that are to be diagnosed in this research. Last decades Next-generation sequencing (NGS) technology has been expanded with advantages in the reliability, data interpretation and costs, sequencing chemistry. Such large improvements can make NGS clinically practical, and NGS technology is presently being developed for use in the field of oncolo-gy. Various clinical applications are discussed, including DNA-sequencing-based mutation detection in inherited cancer syndromes, RNA-sequencing-based detection of spliceogenic variants, DNA-sequencing-based risk modi-fiers and application for pre-implantation genetic diagnosis, and cancer somatic mutation analysis. In this study, neural network classifiers were used to determine patient carcinoma cancer status. Applied neural network architecture is based on a two-level architecture, with each individual network having two outputs based on the normal cell sample or cancer cell sample according to our dataset. [ABSTRACT FROM AUTHOR]

The method of evaluating the physical traits of living creatures by generating essential proteins is known as gene expression. Translation and transcription are the two processes in gene expression. Gene Expression can be captured from DNA or RNA using a variety of approaches. High-throughput gene expression (NGS) data is having multiple advantages over the traditional gene expression dataset. Still one of the major disadvantages of NGS data is it is complex, comprising a large number of genes with few samples; among these thousands of significant genes, disease-causing genes must be identified. This issue should be solved by lowering the data source's dimension to a manageable level. Machine learning (ML) has proven its potential in the research areas of disease identification, and genomics in recent years. Many Machine Learning-based Gene Selection techniques have been described in the literature, to improve dimensionality reduction precision. In this work, a unique clinical support system based on the Relief-Cuckoo Search (CS)-Random Forest (RF) hybrid multi-stage significant gene identification technique is proposed for significant gene selection from the next-generation sequencing (NGS) dataset. This work can be helpful for the accurate identification of sepsis and the relevant genes associated with it, which helps early detection of the disease. This hybrid feature selection technique could be beneficial in later stages of disease treatment, such as disease diagnosis and drug development. The accuracy of the proposed model is 95.23 % which can be helpful for further analysis of various infectious diseases and thus helps the diagnosis procedure more convenient. [ABSTRACT FROM AUTHOR]