Use of peripheral lymphocytes and support vector machine for survival prediction in breast cancer patients

Bai, Fengshan; Wei, Chuanchao; Zhang, Peng; Bi, Dexi; Ge, Meixin; Chen, Qing; Jia, Yijun; Lu, Yin; Wu, Kangning

doi:10.21037/tcr.2018.07.08

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…By extracting prognostic information from clinical, demographic and biochemical data, a prediction model was developed in combination of SVM and random optimiser (RO) and has been reported in Ferroni et al 8 Kim et al 9 focussed on constructing breast cancer recurrence model that could predict 5-year recurrence rate following breast surgery in a Korean population, and the model prediction performance was also compared with other existing models. Bai et al 10 explored the effect of peripheral lymphocytes in identifying prognostic markers among breast cancer patients, and SVM has been used in developing prognostic classifier. Mihaylov et al 11 predicted the survival time of breast cancer patients originally generated from tumour-oriented clinical parameters like age of diagnosis, tumour stage and tumour size.…”

Section: Introductionmentioning

confidence: 99%

Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters

Sarkar,

Mali

2023

DIGITAL HEALTH

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Background Breast cancer is a highly predominant destructive disease among women characterised with varied tumour biology, molecular subgroups and diverse clinicopathological specifications. The potentiality of machine learning to transform complex medical data into meaningful knowledge has led to its application in breast cancer detection and prognostic evaluation. Objective The emergence of data-driven diagnostic model for assisting clinicians in diagnostic decision making has gained an increasing curiosity in breast cancer identification and analysis. This motivated us to develop a breast cancer data-driven model for subtype classification more accurately. Method In this article, we proposed a firefly-support vector machine (SVM) breast cancer predictive model that uses clinicopathological and demographic data gathered from various tertiary care cancer hospitals or oncological centres to distinguish between patients with triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC). Results The results of the firefly-support vector machine (firefly-SVM) predictive model were distinguished from the traditional grid search-support vector machine (Grid-SVM) model, particle swarm optimisation-support vector machine (PSO-SVM) and genetic algorithm-support vector machine (GA-SVM) hybrid models through hyperparameter tuning. The findings show that the recommended firefly-SVM classification model outperformed other existing models in terms of prediction accuracy (93.4%, 86.6%, 69.6%) for automated SVM parameter selection. The effectiveness of the prediction model was also evaluated using well-known metrics, such as the F1-score, mean square error, area under the ROC curve, logarithmic loss and precision–recall curve. Conclusion Firefly-SVM predictive model may be treated as an alternate tool for breast cancer subgroup classification that would benefit the clinicians for managing the patient with proper treatment and diagnostic outcome.

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