Determining relevant biomarkers for prediction of breast cancer using anthropometric and clinical features: A comparative investigation in machine learning paradigm

Publication date: Available online 25 March 2019Source: Biocybernetics and Biomedical EngineeringAuthor(s): Bikesh Kumar SinghAbstractEarly detection of breast cancer plays crucial role in planning and result of associated treatment. The purpose of this article is threefold: (i) to investigate whether or not clinical features obtained using routine blood analysis combined with anthropometric measurements can be utilized for envisaging breast cancer using predictive machine learning techniques; (ii) to explore the role of various machine learning components such as feature selection, data division protocols and classification to determine suitable biomarkers for breast cancer prediction; and (iii) to evaluate a recent database of clinical and anthropometric measurements acquired from normal individuals and individuals suffering from breast cancer. A database consisting of anthropometric and clinical attributes is used in the experiments. Various feature selection and statistical significance analysis methods are used to determine the relevance of various features. Furthermore, popular classifiers such as kernel based support vector machine (SVM), Naïve Bayesian, linear discriminant, quadratic discriminant, logistic regression, K-nearest neighbor (K-NN) and random forest were implemented and evaluated for breast cancer risk prediction using these features. Results of feature selection techniques indicate that among the nine features considered in this study, glucose, age and r...
Source: Biocybernetics and Biomedical Engineering - Category: Biomedical Engineering Source Type: research