3931 Turkish Journal of Computer and Mathematics Education Vol.12 No.3(2021), 3931-3944 A New Way To Prevent Colorectal Cancer Using Supervised Learning Technique Balaji Vicharapu a , Anuradha Chint b , S.R. Chandra Murty Patnala c a,c Research Scholar, Department of CSE,Acharya Nagarjuna University, A.P, India b Assistant professor, Department of CSE, V RSiddhartha Engineering College, India a v.balaji.anu@gmail.com Article History: Received: 10 November 2020; Revised 12 January 2021 Accepted: 27 January 2021; Published online: 5 April 2021 _____________________________________________________________________________________________________ Abstract: The Colorectal cancer prompts to more number of death as of late. The diagnosis of colorectal cancer as early is protected to treat the patient. To distinguish and treat this type of cancer, Colonoscopy is applied ordinarily. Several risk prediction models for colorectal cancer have been created and approved in various populations but colon cancer effecting the young adults. In this research, we projected a Supervised Learning Technique for detecting colorectal cancer in high dimensional information.One of the most important and very popular tool for performing the machine learning tasks that includesnovelty detection,classificationorregression is Support vector machine (SVM). Training the SVM requires large quantity of quadratic programming. Due to memory constraints conventional methods are not directly applied. To overcomethese inadequacies,we introduced, Least Square (LS), Particle Swarm Optimization (PSO), Quadratic Programming and Quantum-behave PSO methods for training SVM.To corroborate the competence and proficiency of our predictable system, it is developed in open source called NCSS Software.The acquiredoutcomesof these approaches are verified on a CCG1.11 Colorectal dataset and related with the particularresolution model. Keywords: Colorectal Cancer, Machine Learning, Support Vector Machine, Particle Swam Optimization, CCG 1.11 and Classification Accuracy ___________________________________________________________________________ 1. Introduction Now a days, cancer deaths is a very dangerous out of all, only 9.6 M peoples are died due to the cancer dieses worldwide in 2018, whatever the reason/ distortion it is. In twenty five years, cancer deaths are decreased by 27 percent in the United States, but this rate is not acceptable. In 2019, more than 6, 00, 000 cancer deaths are predictable and 1.7M or more new cancer cases are recorded with diagnosis. "Cancer is a group of diseases in which cells in the body grow, change, and multiply out of control" [1]. In Pattern recognition domain, cancer detection is a verysignificant research area. This research paper implementing an automatic diagnostic system and classifies cancer patients by building a liner optimal classifier using support vector machine for colorectal cancer. Here four models are used for training SVM such as Quantum-behave PSO, Least Square (LS),Particle Swarm Optimization (PSO), Quadratic Programming methods and also calculated the classification accuracy. Now a day’s usage of classification in medical diagnosis system gradually increases. The most important factors in diagnosis system are patient’s evaluation data and experts decisions.Though, different AI techniques and classifications systems, we can minimize the classification errors those are garnered due to lack of qualified persons and also provide examination of medical information in short time and more exhaustive way. Fig1 illustrates the different steps used in classification design system. As it is outward from the remarksindicators, these steps are dependent. On the opposite, they’redepending andinterconnected, on the consequences, one may go-back to restructurepreviousphases in an effort to improve the completeoverall performance. Research Article Research Article Research Article Research Article Research Article