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Investigating the role of mental health and lifestyle behaviours in the risk of adolescent suicidal behaviours through machine learning

بررسی نقش سلامت روان و رفتارهای شیوه زندگی در خطر رفتارهای خودکشی نوجوانان از طریق یادگیری ماشین

Yanmei Ding, Yanzhao Lei

PLoS ONE 2026 Vol. 21(10), pp. e0358758
https://doi.org/10.1371/journal.pone.0358758

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چکیده

Background Suicidal behaviors among adolescents are a serious global public health concern and are closely linked to both mental health and everyday lifestyle behaviors. Suicidal ideation, planning, and attempts lie along a spectrum of increasing severity. However, how psychological distress and health-related behaviors interact to influence suicide risk is complex and not yet fully understood. This study aimed to identify key mental health and lifestyle factors associated with different stages of adolescent suicidal behaviors using interpretable machine learning methods. Methods Data were obtained from the 2021 Youth Risk Behavior Surveillance System (YRBS), a nationally representative survey of U.S. high school students. After data cleaning and multiple imputation, 16,946 adolescents aged 14–18 years were included in the analysis. Extreme Gradient Boosting (XGBoost) models were developed to predict suicidal ideation, suicide planning, and suicide attempts. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and related metrics. Feature importance was examined using gain-based measures, while SHapley Additive exPlanations (SHAP) and partial dependence plots were used to explore the direction, strength, and nonlinear effects of key predictors. Results All three XGBoost models showed satisfactory predictive performance. Mental health factors, particularly persistent feelings of sadness and self-rated mental health, were the strongest predictors across all stages of suicidal behaviors. Lifestyle behaviors, including sleep duration, physical activity, diet, screen time, and substance use, also contributed meaningfully to risk prediction, although their relative importance differed by outcome. SHAP analyses indicated that greater sadness and poorer mental health were strongly associated with higher suicide risk, whereas adequate sleep and higher levels of physical activity were associated with lower predicted risk. Substance use, including alcohol, cigarette, and marijuana use, played an important role in predicting suicide attempts than suicidal ideation or planning. Partial dependence analyses showed nonlinear and threshold effects, especially for sadness, mental health status, and sleep duration. Conclusion Mental health factors, especially persistent sadness and overall psychological well-being, were among the most prominent factors associated with suicidal behaviors among adolescents, while lifestyle behaviors showed important and stage-specific associations. Interpretable machine learning approaches help identify complex and nonlinear patterns associated with adolescent suicide risk and offer valuable insights to guide targeted, evidence-based prevention efforts.

چکیده به فارسی

زمینه: رفتارهای خودکشی در میان نوجوانان یک نگرانی جدی بهداشت عمومی در سطح جهانی است و ارتباط نزدیکی با سلامت روان و رفتارهای سبک زندگی روزمره دارد. افکار خودکشی، برنامه‌ریزی و اقدام به خودکشی در طیفی با شدت فزاینده قرار دارند. با این حال، نحوه تعامل پریشانی روان‌شناختی و رفتارهای مرتبط با سلامت برای تأثیرگذاری بر خطر خودکشی پیچیده بوده و هنوز به طور کامل درک نشده است. هدف این مطالعه شناسایی عوامل کلیدی سلامت روان و سبک زندگی مرتبط با مراحل مختلف رفتارهای خودکشی در نوجوانان با استفاده از روش‌های یادگیری ماشین قابل تفسیر بود. روش‌ها: داده‌ها از سیستم پایش رفتارهای پرخطر نوجوانان (YRBS) در سال 2021، که یک پیمایش ملی نماینده از دانش‌آموزان دبیرستانی ایالات متحده است، به دست آمد. پس از پاک‌سازی داده‌ها و برآورد چندگانه، 16,946 نوجوان 14 تا 18 ساله در تحلیل وارد شدند. مدل‌های Extreme Gradient Boosting (XGBoost) برای پیش‌بینی افکار خودکشی، برنامه‌ریزی خودکشی و اقدام به خودکشی توسعه یافتند. عملکرد مدل با استفاده از مساحت زیر منحنی مشخصه عملکرد گیرنده (AUC) و معیارهای مرتبط ارزیابی شد. اهمیت ویژگی‌ها با استفاده از معیارهای مبتنی بر gain بررسی شد و برای بررسی جهت، شدت و اثرات غیرخطی پیش‌بین‌های کلیدی از SHapley Additive exPlanations (SHAP) و نمودارهای وابستگی جزئی استفاده گردید. یافته‌ها: هر سه مدل XGBoost عملکرد پیش‌بینی رضایت‌بخشی نشان دادند. عوامل سلامت روان، به ویژه احساسات مداوم غمگینی و ارزیابی ذهنی سلامت روان، قوی‌ترین پیش‌بین‌ها در تمام مراحل رفتارهای خودکشی بودند. رفتارهای سبک زندگی شامل مدت خواب، فعالیت بدنی، رژیم غذایی، زمان استفاده از صفحه نمایش و مصرف مواد نیز به طور معناداری در پیش‌بینی خطر نقش داشتند، اگرچه اهمیت نسبی آن‌ها بسته به پیامد متفاوت بود. تحلیل‌های SHAP نشان داد که غمگینی بیشتر و سلامت روان ضعیف‌تر به طور قوی با افزایش خطر خودکشی مرتبط است، در حالی که خواب کافی و سطوح بالاتر فعالیت بدنی با کاهش خطر پیش‌بینی‌شده همراه بودند. مصرف مواد، از جمله مصرف الکل، سیگار و ماری‌جوانا، نقش مهم‌تری در پیش‌بینی اقدام به خودکشی نسبت به افکار یا برنامه‌ریزی خودکشی داشت. تحلیل‌های وابستگی جزئی اثرات غیرخطی و آستانه‌ای را به ویژه برای غمگینی، وضعیت سلامت روان و مدت خواب نشان داد. نتیجه‌گیری: عوامل سلامت روان، به ویژه غمگینی مداوم و بهزیستی روان‌شناختی کلی، از برجسته‌ترین عوامل مرتبط با رفتارهای خودکشی در نوجوانان بودند، در حالی که رفتارهای سبک زندگی نیز ارتباطات مهم و مرحله‌محور نشان دادند. رویکردهای یادگیری ماشین قابل تفسیر به شناسایی الگوهای پیچیده و غیرخطی مرتبط با خطر خودکشی نوجوانان کمک می‌کنند و بینش‌های ارزشمندی برای هدایت تلاش‌های پیشگیرانه هدفمند و مبتنی بر شواهد ارائه می‌دهند.

ترجمه‌ی خودکار برای آشنایی سریع؛ برای نقل و ارجاع، متن اصلی ملاک است.

موضوع‌ها و کلیدواژه‌ها

بهزیستیخودکشیدانش‌آموزانmental healthافکار خودکشیSuicide attemptadolescent suicidelifestyle habitsself-rated mental healthsuicide planningsuicide risk factors

ارجاع

Ding, Y., & Lei, Y. (2026). Investigating the role of mental health and lifestyle behaviours in the risk of adolescent suicidal behaviours through machine learning. PLoS ONE, 21(10), e0358758. https://doi.org/10.1371/journal.pone.0358758

(Ding & Lei, 2026)

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گفت‌وگو

قوانین پرسش و گفتگو
  1. با احترام و بدون توهین، تحقیر، برچسب‌زنی یا حمله‌ی شخصی با دیگران گفتگو کنید.
  2. از نوشتن هرگونه اطلاعات هویتی یا قابل‌شناسایی مراجعان، بیماران یا اشخاص ثالث، از جمله نام، شماره‌ی تماس، تصویر، نشانی، محل کار یا جزئیات منحصربه‌فرد پرونده خودداری کنید.
  3. اگر درباره‌ی یک تجربه‌ی بالینی سؤال می‌کنید، اطلاعات را تا حد ممکن بی‌نام و کلی بیان کنید تا امکان شناسایی فرد وجود نداشته باشد.
  4. این بخش برای یادگیری و گفت‌وگوی علمی است و جایگزین ارزیابی، تشخیص، مشاوره یا روان‌درمانی شخصی نیست.
  5. در شرایط بحران یا خطر آسیب به خود یا دیگران، به این بخش اتکا نکنید و برای دریافت کمک فوری با اورژانس اجتماعی ۱۲۳ یا صدای مشاور بهزیستی ۱۴۸۰ تماس بگیرید.
  6. انتشار تبلیغات، معرفی خدمات شخصی، شماره‌ی تلفن، شناسه‌ی شبکه‌های اجتماعی، پیوند تبلیغاتی یا درخواست جذب مراجع در این بخش مجاز نیست.
  7. در بیان ادعاهای علمی، به‌ویژه درباره‌ی اثربخشی درمان‌ها، تشخیص یا یافته‌های پژوهشی، تا حد امکان نام منبع معتبر، مقاله یا راهنمای علمی را ذکر کنید.
  8. از ارائه‌ی تشخیص قطعی، تجویز درمان یا دارو و توصیه‌های بالینی شخصی به سایر کاربران خودداری کنید.
  9. پاسخ‌هایی که کارشناسان فکر بنیان می‌دهند با برچسب «پاسخ فکر بنیان» یا «کارشناس فکر بنیان» نمایش داده می‌شوند و سایر نوشته‌ها بیانگر نظر کاربران هستند.
  10. گزارش نوشته‌ی نامناسب: اگر نوشته‌ای حاوی نقض محرمانگی، محتوای آسیب‌زا، توهین، تبلیغ یا تخلف از این قوانین است، گزینه‌ی «گزارش» را انتخاب کنید تا تیم سایت بررسی کند.

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