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Gene data analysis for disease detection using data mining algorithms

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As a result of these promising results, researchers believe that gene expression tests are more important in creating more accurate and efficient diagnostic and classification tools for cancer. In the deoxyribonucleic acid (DNA) system, a gene is transcribed over ribonucleic acid (RNA) during transcription, which is a process known as gene expression (RNA). The study of gene expression data for cancer categorization has recently emerged as an active research subject. This research uses genetic algorithms (GA) to pick a subset of cancer microarray data that contains a meaningful set of genes. Then, standard classifiers like One-R, Bayesian Network, logistic regression, and Support Vector Machine (SVM) are developed based on these specific genes. Gene expression data sets are used to test the performance of these classifiers. According to the results of experiments, combining the confluence of GA and SVM is the most effective approach. In addition, the GA selection process is repeatable.


Ramakrishnan Raman. Gene data analysis for disease detection using data mining algorithms. Cardiometry; Issue 25; December 2022; p.178-181; DOI: 10.18137/cardiometry.2022.25.178181; Available from:


Gene Data,  Data Mining,  RNA,  DNA,  Classification,  Genetic Algorithm
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