• Raj Kumar
• Ranjeev Kumar Chopra

Machine learning is a type of data analysis that automates the creation of analytical models. It's a subset of artificial intelligence predicated on the notion that machines can learn from data, recognize patterns, and make judgments with little or no human interaction Prostate cancer screening with prostate-specific antigen (PSA) has been widely used, however its accuracy is lacking. Building an efficient statistical model using machine learning methods (MLMs) is a potential strategy for improving accuracy. Prostate cancer is the second leading cause of cancer-related mortality among men.. The rate of mortality caused by prostate cancer can be successfully reduced if disease is detected early. Prostate cancer requires adequate diagnostic techniques and instruments due to the high and multi resolution of Magnetic Resonance Imaging (MRIs). Researchers have previously created computer-aided diagnostic (CAD) systems to assist radiologists in detecting problems. In this study, we used unique machine learning techniques to diagnose prostate cancer, including the Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial basis function (RBF), and Gaussian, and Decision Tree. SVM Gaussian Kernel, based on single feature extracting techniques, has the greatest accuracy of 98.34 percent and an AUC of 0.999. SVM Gaussian kernel with texture + morphological, and EFDs + morphological features produce the maximum accuracy of 99.71 percent and AUC of 1.00 when utilizing a combination of feature extraction algorithms.

Decision Tree, Morphological, Support Vector Machine (SVM), Scale Invariant Feature Transform (SIFT), Texture.

"Machine Learning Algorithms For Diagnose Prostate Cancer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 10, page no.709-714, October-2018, Available :http://www.jetir.org/papers/JETIRFH06119.pdf

"Machine Learning Algorithms For Diagnose Prostate Cancer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 10, page no. pp709-714, October-2018, Available at : http://www.jetir.org/papers/JETIRFH06119.pdf