Breast Cancer Prediction Using Supervised Machine Learning Techniques

Abstract

One of the most widely recognized disease in a large portion of the urban communities and the second generally common in rural areas of India is known as Breast Cancer. Every 4 minutes, one lady is determined to have breast cancer growth and one lady dies with breast cancer disease every 13 minutes in India. Over half of breast cancer growth patients in India are encountering stages 3 and 4, where the probabilities of survival are incredibly low, there is a need to assemble a programmed finding framework for quick recognition of disease. For the prediction of breast cancer that whether the patient is suffering from it can be classified with the help of benign and malignant tumor, since we are classifying the data into two hence the classification techniques of machine learning are used in which the machine learning model learnsfrom the past information and can anticipate on the new information. In this paper, the dataset is taken from the UCI repository and relative investigation on the build of the model utilizing Logistic Regression, Support vector machine and Random Forest is done on that dataset. The main objective is to achieve better results among all the algorithms that are used in classifying data with respect to the proficiency and viability of each algorithm in terms of precision, accuracy, and sensitivity. Test outcomes show that the Random Forest is seen to provide the best results for the classification of breast cancer, and it gives an accuracy of 98.60%. This machine learning research is done using the python language and executed in the Scientific Python Development Environment.