Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure

Komal Saxena; Abu Sarwar Zamani; R Bhavani; K V Daya Sagar; Pushpa M Bangare; S Ashwini; Saima Ahmed Rahin

doi:10.1155/2022/2318101

Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure

Biomed Res Int. 2022 Jul 7:2022:2318101. doi: 10.1155/2022/2318101. eCollection 2022.

Authors

Komal Saxena¹, Abu Sarwar Zamani², R Bhavani³, K V Daya Sagar⁴, Pushpa M Bangare⁵, S Ashwini⁶, Saima Ahmed Rahin⁷

Affiliations

¹ Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India.
² Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
³ Institute of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 600124, India.
⁴ Electronics and Computer Science, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
⁵ Department of E&TC, Sinhgad College of Engineering, Savitribai Phule Pune University, Pune, India.
⁶ Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamilnadu, India.
⁷ United International University, Dhaka, Bangladesh.

Abstract

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.

Publication types

Retracted Publication

MeSH terms

Algorithms
Bayes Theorem
Discriminant Analysis
Humans
Mesothelioma* / diagnosis
Mesothelioma* / therapy
Support Vector Machine*