Breast Cancer Detection Using Machine Learning

Breast Cancer Detection Using Machine Learning

Shirin Raut1, Gauri Kalanke2, Shreyash Borode3, Rutuja Awankar4, Prof. J. C. Bambal5

1Shirin Raut, P.R. Pote Patil College of Engineering and Management, Amravati

2Gauri Kalanke, P.R. Pote Patil College of Engineering and Management, Amravati

3Shreyash Borode, P.R. Pote Patil College of Engineering and Management, Amravati

4Rutuja Awankar, P.R. Pote Patil College of Engineering and Management, Amravati

5Prof. J. C. Bambal, P.R. Pote Patil College of Engineering and Management, Amravati

ABSTRACT— Each year number of deaths is increasing extremely because of breast cancer. It is the most frequent type of all cancers and the major cause of death in women worldwide. Any development for prediction and diagnosis of cancer disease is capital important for a healthy life. Consequently, high accuracy in cancer prediction is important to update the treatment aspect and the survivability standard of patients. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique.

In this study, we applied machine learning algorithms: Support Vector Machine (SVM), Random Forest, Logistic Regression, K-Nearest Neighbors’ (KNN) on the Breast Cancer Wisconsin Diagnostic dataset, after obtaining the results, a performance evaluation and comparison is carried out between these different classifiers. The main objective of this research paper is to predict and diagnosis breast cancer, using machine-learning algorithms, and find out the most effective whit respect to confusion matrix, accuracy and precision. It is observed that Support vector Machine outperformed all other classifiers and achieved the highest accuracy (97.2%). All the work is done in the Anaconda environment based on python programming language and Skit-learn library.