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

Abstract

Machine Learning Models for Predicting Breast Cancer Risk in Women Exposed to Blue Light from Digital Screens Mortazavi, S. A., Tahmasebi, S., Parsaei, H., Taleie, A., Faraz, M., Rezaianzadeh, A., Zamani, A., Zamani, A., Mortazavi, S. M. J. (2022). Machine Learning Models for Predicting Breast Cancer Risk in Women Exposed to Blue Light from Digital Screens. Journal of Biomedical Physics and Engineering, 12(6), 637-644. doi: 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. Open access paper: jbpe.sums.ac.ir

AI evidence extraction

Main findings

Using questionnaire/interview-derived parameters related to exposure to ionizing and non-ionizing radiation (including blue light and screen time), Random Forest, SVM, and MLPNN models classified breast cancer cases vs controls with mean sensitivity >97.2%, mean specificity >96.4%, and average accuracy >97.1%.

Outcomes measured

• Breast cancer risk/status (case vs control classification)
• Machine learning model performance (sensitivity, specificity, accuracy)

Limitations

• Exposure assessment based on face-to-face interviews and questionnaire data
• Study focuses on predictive model performance rather than estimating an exposure–disease effect size

{
    "study_type": "case_control",
    "exposure": {
        "band": null,
        "source": "digital screens",
        "frequency_mhz": null,
        "sar_wkg": null,
        "duration": null
    },
    "population": "Women (breast cancer cases and age- and gender-matched controls)",
    "sample_size": 603,
    "outcomes": [
        "Breast cancer risk/status (case vs control classification)",
        "Machine learning model performance (sensitivity, specificity, accuracy)"
    ],
    "main_findings": "Using questionnaire/interview-derived parameters related to exposure to ionizing and non-ionizing radiation (including blue light and screen time), Random Forest, SVM, and MLPNN models classified breast cancer cases vs controls with mean sensitivity >97.2%, mean specificity >96.4%, and average accuracy >97.1%.",
    "effect_direction": "unclear",
    "limitations": [
        "Exposure assessment based on face-to-face interviews and questionnaire data",
        "Study focuses on predictive model performance rather than estimating an exposure–disease effect size"
    ],
    "evidence_strength": "low",
    "confidence": 0.7399999999999999911182158029987476766109466552734375,
    "peer_reviewed_likely": "yes",
    "keywords": [
        "blue light",
        "digital screens",
        "screen time",
        "breast cancer",
        "machine learning",
        "Random Forest",
        "support vector machine",
        "multi-layer perceptron",
        "non-ionizing radiation",
        "ionizing radiation",
        "questionnaire",
        "case-control"
    ],
    "suggested_hubs": []
}

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