Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis

Abstract

Breast cancer is one of the leading causes of death among women, more so than all other cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the disease, changing treatment procedures and different patient population samples. Diagnostic techniques with better performance are very important for personalized care and treatment and to reduce and control the recurrence of cancer. The main objective of this research was to select feature selection techniques using correlation analysis and variance of input features before passing these significant features to a classification method. We used an ensemble method to improve the classification of breast cancer. The proposed approach was evaluated using the public WBCD dataset (Wisconsin Breast Cancer Dataset). Correlation analysis and principal component analysis were used for dimensionality reduction. Performance was evaluated for well-known machine learning classifiers, and the best seven classifiers were chosen for the next step. Hyper-parameter tuning was performed to improve the performances of the classifiers. The best performing classification algorithms were combined with two different voting techniques. Hard voting predicts the class that gets the majority vote, whereas soft voting predicts the class based on highest probability. The proposed approach performed better than state-of-the-art work, achieving an accuracy of 98.24%, high precision (99.29%) and a recall value of 95.89%.

Breast Cancer Diagnosis with Machine Learning Techniques

“…In this study, unlike previous studies, the step of detection and exclusion of outliers was added to the data set. When the table is examined, it is seen that the results of the studies with feature selection [13,[16][17][22][23][24] are considerably higher than the results of the studies without feature selection [14,[16][17][18][19][20][21][25][26]. PCA, KPCA, etc.…”

“…İbrahim et.al. [23] in their study, before passing the features to the classification step, made feature selection using the variance of the input features and correlation analysis. An ensemble method was selected from seven classification algorithms to improve breast cancer classification.…”

Comparing Data Mining Algorithms for Breast Cancer Diagnosis

“…The selected features are used as input for developing ML-based predictive models (37,38). By recognizing patterns within the large amounts of data, it may be applied to gain more insight into the diseases and produce knowledge that can potentially inspire further research in many areas of medicine (39)(40)(41).…”

Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia

“…One of the finest methods to deduce high-potential candidates is machine-learning-based categorization, which acts as a researcher's bias-free technique to discover traits that may function as diagnostic and prognostic biomarkers, giving an opportunity to uncover underlying pathophysiological principles and mechanisms (25)(26)(27)(28). Herein, we performed machine learning-based classification to identify diagnostic and prognostic markers for PAC.…”