Breast cancer detection using K-nearest neighbors data mining method obtained from the bow-tie antenna dataset

2017 ◽  
Vol 27 (6) ◽  
pp. e21098 ◽  
Author(s):  
Emine Avşar Aydın ◽  
Mümine Kaya Keleş
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Cancer Detection ◽  
Nearest Neighbors ◽  
Breast Cancer Detection ◽  
Mining Method ◽  
K Nearest Neighbors ◽  
Data Mining Method ◽  
Bow Tie

Breast Cancer Detection Using Random Forest Classifier

2021 ◽  
pp. 85-98
Author(s):  
Pavithra Suchindran ◽  
Vanithamani R. ◽  
Judith Justin
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Cancer Detection ◽  
Region Of Interest ◽  
Texture Features ◽  
Speckle Noise ◽  
Random Forest Classifier ◽  
Breast Cancer Detection ◽  
K Nearest Neighbors ◽  
Cad System

Breast cancer is the second most prevalent type of cancer among women. Breast ultrasound (BUS) imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities in the breast. To improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is helpful for breast cancer detection and classification. Normally, a CAD system consists of four stages: pre-processing, segmentation, feature extraction, and classification. In this chapter, the pre-processing step includes speckle noise removal using speckle reducing anisotropic diffusion (SRAD) filter. The goal of segmentation is to locate the region of interest (ROI) and active contour-based segmentation and fuzzy C means segmentation (FCM) are used in this work. The texture features are extracted and fed to a classifier to categorize the images as normal, benign, and malignant. In this work, three classifiers, namely k-nearest neighbors (KNN) algorithm, decision tree algorithm, and random forest classifier, are used and the performance is compared based on the accuracy of classification.

scholarly journals A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models

2021 ◽  
pp. 45-59
Author(s):  
Shler Farhad Khorshid ◽  
Adnan Mohsin Abdulazeez ◽  
Amira Bibo Sallow
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Mining ◽  
Nearest Neighbors ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Wide Range

Breast cancer is one of the most common diseases among women, accounting for many deaths each year. Even though cancer can be treated and cured in its early stages, many patients are diagnosed at a late stage. Data mining is the method of finding or extracting information from massive databases or datasets, and it is a field of computer science with a lot of potentials. It covers a wide range of areas, one of which is classification. Classification may also be accomplished using a variety of methods or algorithms. With the aid of MATLAB, five classification algorithms were compared. This paper presents a performance comparison among the classifiers: Support Vector Machine (SVM), Logistics Regression (LR), K-Nearest Neighbors (K-NN), Weighted K-Nearest Neighbors (Weighted K-NN), and Gaussian Naïve Bayes (Gaussian NB). The data set was taken from UCI Machine learning Repository. The main objective of this study is to classify breast cancer women using the application of machine learning algorithms based on their accuracy. The results have revealed that Weighted K-NN (96.7%) has the highest accuracy among all the classifiers.

Sign in / Sign up

Export Citation Format

Share Document