• Goutam R
• Sachitha S

Machine learning is as often as possible utilised in clinical applications, for example, discovery of the kind of dangerous cells. Breast malignant growth speaks to one of the ailments that causes a high number of deaths consistently. It is the most widely recognised sort of malignant growth and the fundamental driver of women demises around the world. The harmful cells are of two types Benign (B) or Malignant (M). There are numerous algorithms for characterisation and expectation of breast cancer: Support Vector Machine (SVM), Decision Tree (CART), Naive Bayes (NB) and k Nearest Neighbors (kNN). In this venture, Support Vector Machine (SVM) on the Wisconsin Breast Cancer dataset is utilised. The dataset is likewise prepared with the different algorithms: KNN, Naives Bayes and CART and the exactness of expectation for every algorithm is looked at.

"Prediction and Diagnosis of Breast Cancer Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.105-110, April-2020, Available :http://www.jetir.org/papers/JETIR2004215.pdf

"Prediction and Diagnosis of Breast Cancer Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp105-110, April-2020, Available at : http://www.jetir.org/papers/JETIR2004215.pdf