Breast Cancer Prediction Using Machine Learning Algorithms

Breast cancer (BC) is the most frightful disease. It is becoming more severe day by day among the women. Death cases are increasing drastically. Two out of eight women die due to BC. Two types of tumors are used in the dataset for the classification of BC. Tumors are named as benign (noncancerous) and malignant (cancerous). Medical researchers use different types of scans like mammography and CT to find out the occurrence of BC. But by using Machine learning (ML) models we can predict the disease before its occurrence. Early prediction of BC could decrease the rate of death. The survival rate of the patients is also increasing. Women who live in polluted areas have a higher risk of BC because the presence of lead and mercury is very high in the environment, Pollution may cause cancer. In this paper three ML methods are utilized to forecast BC, such as Logistic Regression (LR), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). These algorithms are supervised learning algorithms of ML. Total dataset of 569 rows and 32 columns and parameters such as id, diagnosis, symmetry_worst fractal_dimension_worst, etc. LR model performed better with 99% accuracy.