Timely Recognition of Breast Cancer (BC) Via Support Vector Machines (SVMs) Algorithms-A Study

  • Rajesh Kumar Maurya, Sanjay Kumar Yadav, Pragya Tiwari


As we all are aware that now a days in almost all parts of world, BC is most prominent causes of lady’s casualty on the earth  and characterized as a major heterogeneous and deadliest disease that is leading causes of loss of life in human being. Machine Learning (ML) for BC data classification and Artificial Intelligence (AI) have proven biomedical research approaches that have mainly been used for the cancerous tumor classification and detection task. Participation of machine classification allows the Medical Practitioners (MPs) and the surgeons a second opinion and it may save the MPs time. Certainly the methods applied for ML may increase one’s perceptive for BC prediction. At present SVMs are important ML tools for classification of genomics data. SVMs are successfully applied in biomedical applications and getting, good popularity in Computational Biology and Bioinformatics. SVM methods have been applied as an essential tool by physicians for prompt anticipation and detection of harmful malignant body cells those have been designed and initiate in the body. These days, as advancements in evolving computing tools lead to large amounts of genomic and epigenetic data, the SVM classification function has expanded its use in cancer genomes feature patterns studies. We intend to study the efficiency of SVM and its eminent perspective in the genomic applications and pattern based search of BC.

How to Cite
Pragya Tiwari, R. K. M. S. K. Y. (2020). Timely Recognition of Breast Cancer (BC) Via Support Vector Machines (SVMs) Algorithms-A Study. International Journal of Advanced Science and Technology, 29(5s), 365 - 375. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/7171