Open AccessComparative Study of Machine Learning Algorithms for Breast Cancer Prediction - A Review
Author(s) -
Akshya Yadav,
Imlikumla Jamir,
Raj Rajeshwari Jain,
Mayank Sohani
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit1952278
Subject(s) - random forest , machine learning , algorithm , breast cancer , naive bayes classifier , artificial intelligence , support vector machine , computer science , cancer , classifier (uml) , statistical classification , medicine
Cancer has been characterized as one of the leading diseases that causes death in humans. Breast cancer being a subtype of cancer causes death in one out of every eight women worldwide. The solution to counter this is by conducting early and accurate diagnosis for faster treatment. To achieve such accuracy in a short span of time proves difficult with existing techniques. In this paper, different machine learning algorithms which can be used as tools by physicians for early and effective detection and prediction of cancerous cells have been studied and introduced. The different algorithms introduced here are ANN, DT, Random Forest (RF), Naive Bayes Classifier (NBC), SVM and KNN. These algorithms are trained with a dataset that contain parameters describing the tumor of a person having breast cancer and are then used to classify and predict whether the cell is cancerous.