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 includes novelty detection, classification or regression 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 overcome these 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 acquired outcomes of these approaches are verified on a CCG1.11 Colorectal dataset and related with the particular resolution model.