TY - JOUR ID - 121503 TI - Breast cancer detection using machine learning algorithms JO - International Journal of Research in Industrial Engineering JA - RIEJ LA - en SN - 2783-1337 AU - Shastri, R. AU - Pradeep, N. AU - Rao Mangalore, K. K. AU - Rajpal, B. AU - Prasad, N. AD - Department of MCA, School of Computer Science and IT, Jain (deemed-to-be) University, Bengaluru, India. Y1 - 2020 PY - 2020 VL - 9 IS - 3 SP - 235 EP - 246 KW - Machine Learning KW - K-Nearest Neighbors KW - Support Vector Machine KW - Decision Tree Classifier KW - Jupyter DO - 10.22105/riej.2020.259298.1155 N2 - Breast cancer has been the riskiest malignancy among ladies around the world. Nearly 2 million new cases were diagnosed in 2018. The main problem in the detection of breast cancer is to find how tumors turn into malignant or benign and we can do this with the help of machine learning techniques as they provide an appropriate result. According to research, an experienced physician can diagnose cancer with 79% accuracy while using machine learning techniques provides an accuracy of 91%. In this work, machine learning techniques have been applied which include K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM), and Decision Tree Classifier (DT). To predict whether the cause is benign or malignant we have used the breast cancer dataset. The SVM classifier gives more accurate and precise results as compared to others, and this classifier is trained with the larger datasets. UR - https://www.riejournal.com/article_121503.html L1 - https://www.riejournal.com/article_121503_65c637bdcdd08d268dfd5e0b84f5b6d0.pdf ER -