TY - JOUR
T1 - Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure
AU - Saxena, Komal
AU - ABU SARWAR ZAMANI, null
AU - Bhavani, R.
AU - Sagar, K. V.Daya
AU - Bangare, Pushpa M.
AU - Ashwini, S.
AU - Rahin, Saima Ahmed
N1 - Publisher Copyright:
© 2022 Komal Saxena et al.
PY - 2022
Y1 - 2022
N2 - Mesothelioma is a dangerous, violent cancer, which forms a protecting layer around inner tissues such as the lungs, stomach, and heart. We investigate numerous AI methodologies and consider the exact DM conclusion outcomes in this study, which focuses on DM determination. K-nearest neighborhood, linear-discriminant analysis, Naive Bayes, decision-tree, random forest, support vector machine, and logistic regression analyses have been used in clinical decision support systems in the detection of mesothelioma. To test the accuracy of the evaluated categorizers, the researchers used a dataset of 350 instances with 35 highlights and six execution measures. LDA, NB, KNN, SVM, DT, LogR, and RF have precisions of 65%, 70%, 92%, 100%, 100%, 100%, and 100%, correspondingly. In count, the calculated complication of individual approaches has been evaluated. Every process is chosen on the basis of its characterization, exactness, and calculated complications. SVM, DT, LogR, and RF outclass the others and, unexpectedly, earlier research.
AB - Mesothelioma is a dangerous, violent cancer, which forms a protecting layer around inner tissues such as the lungs, stomach, and heart. We investigate numerous AI methodologies and consider the exact DM conclusion outcomes in this study, which focuses on DM determination. K-nearest neighborhood, linear-discriminant analysis, Naive Bayes, decision-tree, random forest, support vector machine, and logistic regression analyses have been used in clinical decision support systems in the detection of mesothelioma. To test the accuracy of the evaluated categorizers, the researchers used a dataset of 350 instances with 35 highlights and six execution measures. LDA, NB, KNN, SVM, DT, LogR, and RF have precisions of 65%, 70%, 92%, 100%, 100%, 100%, and 100%, correspondingly. In count, the calculated complication of individual approaches has been evaluated. Every process is chosen on the basis of its characterization, exactness, and calculated complications. SVM, DT, LogR, and RF outclass the others and, unexpectedly, earlier research.
UR - https://www.scopus.com/pages/publications/85134721592
U2 - 10.1155/2022/2318101
DO - 10.1155/2022/2318101
M3 - Article
C2 - 35845952
AN - SCOPUS:85134721592
SN - 2314-6133
VL - 2022
JO - BioMed Research International
JF - BioMed Research International
M1 - 2318101
ER -
