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

• Autorzy: Singh Bikesh Kumar

Identyfikatory

DOI

10.1016/j.bbe.2019.03.001

• Języki publikacji: EN

Abstrakty

EN

Early 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 resistin are found to be most relevant and effective biomarkers for breast cancer prediction. Further, when these three features are used for classification, the medium K-NN classifier achieves the highest classification accuracy of 92.105% followed by medium Gaussian SVM which achieves classification accuracy of 83.684% under hold out data division protocol.

Słowa kluczowe

EN

breast cancer biomarkers; machine learning; expert system; clinical features; feature selection

PL

biomarkery raka piersi; uczenie maszynowe; system ekspertowy; cechy kliniczne; selekcja cech

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 2
• Strony: 393--409
• Opis fizyczny: Bibliogr. 43 poz., rys., tab., wykr.

Twórcy

autor

Singh Bikesh Kumar

• Department of Biomedical Engineering, National Institute of Technology, Raipur, G.E. Road, Raipur, Chhattisgarh 492010, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).