Document Type : Original Research

Authors

1 MD, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

2 MD, Breast Cancer Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

3 PhD, Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

4 PhD, Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

5 MSc, Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

6 PhD, Department of Epidemiology, Shiraz University of Medical Sciences, Shiraz, Iran

7 PhD, Department of Statistics, Shiraz University of Medical Sciences, Shiraz, Iran

10.31661/jbpe.v0i0.2105-1341

Abstract

Background: Nowadays, there is a growing global concern over rapidly increasing screen time (smartphones, tablets, and computers). An accumulating body of evidence indicates that prolonged exposure to short-wavelength visible light (blue component) emitted from digital screens may cause cancer. The application of machine learning (ML) methods has significantly improved the accuracy of predictions in fields such as cancer susceptibility, recurrence, and survival.
Objective: To develop an ML model for predicting the risk of breast cancer in women via several parameters related to exposure to ionizing and non-ionizing radiation.
Material and Methods: In this analytical study, three ML models Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron Neural Network (MLPNN) were used to analyze data collected from 603 cases, including 309 breast cancer cases and 294 gender and age-matched controls. Standard face-to-face interviews were performed using a standard questionnaire for data collection.
Results: The examined models RF, SVM, and MLPNN performed well for correctly classifying cases with breast cancer and the healthy ones (mean sensitivity> 97.2%, mean specificity > 96.4%, and average accuracy > 97.1%).
Conclusion: Machine learning models can be used to effectively predict the risk of breast cancer via the history of exposure to ionizing and non-ionizing radiation (including blue light and screen time issues) parameters. The performance of the developed methods is encouraging; nevertheless, further investigation is required to confirm that machine learning techniques can diagnose breast cancer with relatively high accuracies automatically.

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