Boundaries tuned support vector machine (BT-SVM) classifier for cancer prediction from gene selection. Issue 7 (19th May 2022)

• Record Type: Journal Article
• Authors: Abinash, M. J.; Vasudevan, V.
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 25:Issue 7(2022)
• Page Start: 794
• Page End: 807
• Publication Date: 2022-05-19
• Subjects: Boundaries tuned support vector machines (BT-SVM) classifier -- particle swarm optimization -- wrapper model algorithm -- principal component analysis; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2021.1981300
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: In recent days, the identified genes which are detecting cancer-causing diseases are plays a crucial part in the microarray data analysis. Huge volume of data required since the disease changed often. Conventional data mining techniques are lacking in space concern and time complexity. Based on big data the proposed work is executed. Using the ISPCA – Improved Supervised Principal Component Analysis, feature extraction is developed in this study. For gene expression, co-variance matrix is generated and through feature selection cancer classification is performed by IPSCA. Further feature selection process by boundaries tuned support vector machines (BT-SVM) classifier and modified particle swarm optimization with novel wrapper model algorithm are performed. The experimentation is carried out by utilizing different datasets like leukaemia, breast cancer dataset, brain cancer, colon, and lung carcinoma from the UCI repository. The proposed work is executed on six benchmark dataset for DNA microarray data in terms of accuracy, recall, and precision to evaluate the performance of the proposed work. For evaluating the proposed work effectiveness, it is compared with various traditional techniques and resulted in optimum accuracy, recall, precision and training time with and without feature selection effectively.