Breast Cancer Detection Using Machine Learning Algorithms
- Resource Type
- Conference
- Authors
- Sharma, Shubham; Aggarwal, Archit; Choudhury, Tanupriya
- Source
- 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS) Computational Techniques, Electronics and Mechanical Systems (CTEMS), 2018 International Conference on. :114-118 Dec, 2018
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Breast cancer
Machine learning algorithms
Training
Classification algorithms
Machine learning
Breast Cancer
random forest
k-Nearest-Neighbor
naive bayes
- Language
The most frequently occuring cancer among Indian women is breast cancer. There is a chance of fifty percent for fatality in a case as one of two women diagnosed with breast cancer die in the cases of Indian women [1]. This paper aims to present comparison of the largely popular machine learning algorithms and techniques commonly used for breast cancer prediction, namely Random Forest, kNN (k-Nearest-Neighbor) and Naïve Bayes. The Wisconsin Diagnosis Breast Cancer data set was used as a training set to compare the performance of the various machine learning techniques in terms of key parameters such as accuracy, and precision. The results obtained are very competitive and can be used for detection and treatment.