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Mortazavi, Seyed Alireza, Tahmasebi, Sedigheh, Parsaei, Hossein, Taleie, Abdorasoul, Faraz, Mehdi, Rezaianzadeh, Abbas, Zamani, Atefeh, Zamani, Ali, Mortazavi, Seyed Mohammad Javad. (1401). Machine Learning Models for Predicting Breast Cancer Risk in Women Exposed to Blue Light from Digital Screens. سامانه مدیریت نشریات علمی, 12(6), 637-644. doi: 10.31661/jbpe.v0i0.2105-1341
Seyed Alireza Mortazavi; Sedigheh Tahmasebi; Hossein Parsaei; Abdorasoul Taleie; Mehdi Faraz; Abbas Rezaianzadeh; Atefeh Zamani; Ali Zamani; Seyed Mohammad Javad Mortazavi. "Machine Learning Models for Predicting Breast Cancer Risk in Women Exposed to Blue Light from Digital Screens". سامانه مدیریت نشریات علمی, 12, 6, 1401, 637-644. doi: 10.31661/jbpe.v0i0.2105-1341
Mortazavi, Seyed Alireza, Tahmasebi, Sedigheh, Parsaei, Hossein, Taleie, Abdorasoul, Faraz, Mehdi, Rezaianzadeh, Abbas, Zamani, Atefeh, Zamani, Ali, Mortazavi, Seyed Mohammad Javad. (1401). 'Machine Learning Models for Predicting Breast Cancer Risk in Women Exposed to Blue Light from Digital Screens', سامانه مدیریت نشریات علمی, 12(6), pp. 637-644. doi: 10.31661/jbpe.v0i0.2105-1341
Mortazavi, Seyed Alireza, Tahmasebi, Sedigheh, Parsaei, Hossein, Taleie, Abdorasoul, Faraz, Mehdi, Rezaianzadeh, Abbas, Zamani, Atefeh, Zamani, Ali, Mortazavi, Seyed Mohammad Javad. Machine Learning Models for Predicting Breast Cancer Risk in Women Exposed to Blue Light from Digital Screens. سامانه مدیریت نشریات علمی, 1401; 12(6): 637-644. doi: 10.31661/jbpe.v0i0.2105-1341

Machine Learning Models for Predicting Breast Cancer Risk in Women Exposed to Blue Light from Digital Screens

Journal of Biomedical Physics and Engineering
دوره 12، شماره 6، اسفند 2022، صفحه 637-644    اصل مقاله (740.99 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2105-1341
نویسندگان
Seyed Alireza Mortazavi1؛ Sedigheh Tahmasebi2؛ Hossein Parsaei* 3، 4؛ Abdorasoul Taleie2؛ Mehdi Faraz5؛ Abbas Rezaianzadeh6؛ Atefeh Zamani7؛ Ali Zamani3؛ Seyed Mohammad Javad Mortazavi3
1MD, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
2MD, Breast Cancer Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
3PhD, Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
4PhD, Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
5MSc, Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
6PhD, Department of Epidemiology, Shiraz University of Medical Sciences, Shiraz, Iran
7PhD, Department of Statistics, Shiraz University of Medical Sciences, Shiraz, Iran
چکیده
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.
کلیدواژه‌ها
Artificial Intelligence؛ Breast Cancer؛ Digital Screens؛ Screen Time؛ Visible Light؛ Blue Light؛ Prognosis Prediction؛ Smartphone؛ Circadian Clocks
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