Predicting Breast Cancer Using Machine Learning

Abstract

Malignancy is a type of sickness which occurs due to the change in the growth of cells in the body and increment past typical development and control. Bosom or breast malignancy is one of the continuous sorts of disease. The anticipation of bosom malignancy repeat is profoundly required to rise the endurance pace of patient experiencing bosom disease. With the headway of innovation and AI methods, the malignancy analysis and recognition exactness have improved. AI (ML) procedures offer different probabilistic and factual strategies that permit savvy frameworks to gain recurring past encounters to recognize and distinguish designs from a dataset. The exploration work exhibited a review of the AI procedures in malignancy sickness by applying learning calculations on bosom disease by using the dataset from the Wisconsin diagnostic breast cancer—support vector machine, random forest, K-nearest neighbor, and decision tree. The outcome result shows that Random Forest performs superior to different procedures.