Dimensionality Reduction using PCA and K-Means Clustering for Breast Cancer Prediction

Abstract

Breast cancer is the most important cause of death among women. A prediction of breast cancer in early stage provides a greater possibility of its cure. It needs a breast cancer prediction tool that can classify a breast tumor whether it was a harmful malignant tumor or un-harmful benign tumor. In this paper, two algorithms of machine learning, namely Support Vector Machine and Extreme Gradient Boosting technique will be compared for classification purpose. Prior to the classification, the number of data attribute will be reduced from the raw data by extracting features using Principal Component Analysis. A clustering method, namely K-Means is also used for dimensionality reduction besides the Principal Component Analysis. This paper will present a comparison among four models based on two dimensionality reduction methods combined with two classifiers which applied on Wisconsin Breast Cancer Dataset. The comparison will be measured by using accuracy, sensitivity and specificity metrics evaluated from the confusion matrices. The experimental results have indicated that the K-Means method, which is not usually used for dimensionality reduction can perform well compared to the popular Principal Component Analysis.

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

“…Through the experimental results, it has been shown that the random selection of RF-PCA provides better accuracy than other methods. Additionally, Jamal et al in [76] focused on the number of features for the classification of breast cancer from the original White Blood Cell (WBC) data set can be reduced by the feature extracting. The metric measurement results that the dimensionality reduction using the K-means cluster is almost as good as PCA.…”

Comparative Analysis of Dimension Reduction Methods

“…Optimum , paremetrelerinin tespiti için . gizli ünitenin aktivasyonu (2) (1) , (1) değerleri ise giriş ile gizli katman arasındaki yani kodlayıcı olarak adlandırılan kısımdaki ağın ağırlık parametreleridir. Denklem 9'da oto-kodlayıcının toplam maliyet fonksiyonu gösterilmiştir.…”

“…analizi birçok alanı kapsamaktadır. Veri Bu çalışmada, doğrusal boyut azaltma tekniklerinden PCA [1], [2], LDA [3], [4] ve doğrusal olmayan boyut azaltma tekniklerinden Autoencoder yöntemleri incelenmiştir [5], [6]. Bu yöntemlerin performansları, literatürde sık kullanılan MNIST [7] veri kümesi (el yazısıyla yazılmış rakamlar) kullanılarak elde edilmiş, sınıflandırma doğrulukları ve hesaplama süreleri kıyaslanmıştır.…”

Cancer Risk Analysis Based on Improved Probabilistic Neural Network

“…The experiment was performed using the Wisconsin dataset, while the result showed that KNN outperforms Naïve Bayes with the higher accuracy of 97.51% compared to that of 96.19%. Another breast cancer prediction work has been reported in Jamal et al ( 2018 ), in which authors utilized the hybrid technique of Extreme Gradient Boosting technique and Support Vector Machine. Furthermore, they also applied the Principle Component Analysis (PCA) and K-Means Clustering method to reduce the problem dimensionality.…”