文章基本信息

标题：Microarray Baseddisease Prediction Using Deep Learning Techniques
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作者：V Gokulakrishnan ; K Madhubala ; R Selvasarathi 等
期刊名称：International Journal of Advances in Engineering and Management
电子版ISSN：2395-5252
出版年度：2021
卷号：3
期号：4
页码：237-242
DOI：10.35629/5252-03045560
语种：English
出版社：IJAEM JOURNAL
摘要：The DNA microarray technology has modernized the approach of biology research in such a way that scientists can now measure the expression levels of thousands of genes simultaneously in a single experiment. Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. Diseases classification with gene expression data is known to include the keys for addressing the fundamental harms relating to diagnosis and discovery. The recent introduction of DNA microarray technique has complete simultaneous monitoring large number of gene expressions possible. With this large quantity of gene expression data, experts have started to discover the possibilities of disease classification using gene expression data. Quite a large number of methods have been planned in recent years with hopeful results. In order to gain insight into the disease classification difficulty, it is necessary to get a closer look at the problem, the proposed solutions and the associated issues all together. This present a comprehensive searching method, clustering method and classification method such as Pattern similarity search, Spatial Expectation Maximization, nearest neighbour classification and estimate them based on their evaluation time, classification accuracy and ability to reveal biologically meaningful gene information. Based on our multiclass classification method to diagnosis the diseases such as Cancer (Lung, Blood, Breast and Skin) diseases and other diseases and also find severity levels of diseases and also prescribe the medicine for affected diseases. This experimental results show that classifier performance through graphs with improved accuracy.
关键词：Gene selection;cancer microarray data;cuckoo search;multi-objective;evolutionary operators