This paper is published in Volume-5, Issue-3, 2019
Area
Machine Learning
Author
Pooja Mudgil
Co-authors
Mohit Garg, Vaibhav Chhabra, Parikshit Sehgal, Jyoti
Org/Univ
Bhagwan Parshuram Institute of Technology, New Delhi, Delhi, India
Pub. Date
14 May, 2019
Paper ID
V5I3-1326
Publisher
Keywords
Machine learning, Classification, Naïve Bayes, K Nearest Neighbour, Decision tree, Logistic regression, Random forest

Citationsacebook

IEEE
Pooja Mudgil, Mohit Garg, Vaibhav Chhabra, Parikshit Sehgal, Jyoti. Breast cancer prediction algorithms analysis, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Pooja Mudgil, Mohit Garg, Vaibhav Chhabra, Parikshit Sehgal, Jyoti (2019). Breast cancer prediction algorithms analysis. International Journal of Advance Research, Ideas and Innovations in Technology, 5(3) www.IJARIIT.com.

MLA
Pooja Mudgil, Mohit Garg, Vaibhav Chhabra, Parikshit Sehgal, Jyoti. "Breast cancer prediction algorithms analysis." International Journal of Advance Research, Ideas and Innovations in Technology 5.3 (2019). www.IJARIIT.com.

Abstract

Machine learning which an application of Artificial intelligence (AI) is makes the system capable to automatically learn through the environment without being explicitly programmed. It is widely used in various domains like classification and prediction processes. This paper basically compares classifier algorithms like-Naïve Bayes, K Nearest Neighbour, Decision tree, Logistic Regression, Random Forest, Support Vector Machine (SVM). These algorithms predict chances of breast cancer and are programmed in python language. The implementation procedure shows that the performance of any classification algorithm is based on the type of attributes of datasets and their characteristics. The main aim of this paper is to do the comparison of these algorithms on the basis of the accuracy. The goal is to classify whether breast cancer is “Benign” or “Malignant”.
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