Year: 2020
Type: conference-paper
Abstract: The panorama of microarray innovation has empowered the scientists to quickly gauge the degrees of thousands of qualities communicated in an organic tissue test in a solitary analysis. One significant use of this microarray innovation is to order the tissue tests utilizing their quality articulation profiles, recognizing a few sorts of disease. In this paper an endeavor is made to characterize benchmark colon malignancy microarray dataset utilizing arrangement calculations of AI. The dataset comprises of qualities from quality articulation information which are profoundly communicated or stifled. Accordingly five AI order calculations in particular SVM, Decision Tree, Random Forest, Gaussian Naive Bayes and Gradient Boosting are used to group the samples into cancerous and noncancerous. The comparative perusal of the algorithms are based on, F-Measure, Accuracy, Precision, and Recall. Results obtained evince Random Forest outclass other four calculations with the most astounding accuracy exactness of 89.47%. The results are verified in proper and systematic manner using ROC curves.
Source: Procedia Computer Science
Authors: Merry K P, Kajal Tanchak
Institution: Marwadi University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W3033774463