Towards Transparent Mental Health AI: An Explainable Multi-Branch Ensemble Model for Suicide Ideation Detection on Social Media

Towards Transparent Mental Health AI: An Explainable Multi-Branch Ensemble Model for Suicide Ideation Detection on Social Media | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Towards Transparent Mental Health AI: An Explainable Multi-Branch Ensemble Model for Suicide Ideation Detection on Social Media Md.Faishal Ahmed Rudro, Anzim Hasan Nabil, Jarin Sultana Asha, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7965037/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Early detection of suicidal ideation via web text is a key to early psychological treatment and suicide prevention. Nevertheless, traditional deep learning models tend to neglect all at once emotional tone, linguistic subtlety, and contextual meaning. To address this deficiency, we introduce a novel multi-branch ensemble model combining CNN, BiLSTM, CNN–LSTM, LightGBM, and XGBoost models by weighted soft-voting fusion strategy. The model combines semantic embeddings, TF–IDF statistical features, and sentiment polarity features to facilitate deep contextual learning and explainable linguistic inference. Strong exploratory data analysis and statistical testing (t-tests, sentiment profiling, keyword frequency tests) substantiate significant emotional and compositional differences in suicidal and non-suicidal posts in the Kaggle Suicide and Depression Detection Dataset (232K Reddit posts). Experimental performance shows that the put forward ensemble outperforms all the baselines with test accuracy of 95.36% and AUC of 0.989. In addition, inclusion of Explainable AI (XAI) elements—LIME and Attention-based visualizations—ensures a clear explanation of feature importance and emotional attribution, facilitating enhanced clinical trust and interpretability. The suggested hybrid framework provides a solid and interpretable basis to real-time mental health risk assessment, connecting predictive performance and ethical interpretability in AI-based suicide prevention. Suicide Ideation Detection Ensemble Deep Learning Explainable AI CNN–LSTM Sentiment Analysis Mental Health Monitoring Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7965037","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540148901,"identity":"a4b3ead0-599a-4e2b-a0db-87b198d80071","order_by":0,"name":"Md.Faishal Ahmed Rudro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYFCCBIYPDAZAkh3I5mFgkAOJHXiAXwvjDLAWZogWY7CWBIJaGBBaEhsgVuMGuu0JjA0/Cury+Jt5n0m83WOTPj/s8EOgLXZyug3YtZidecDY2GNwuFjiMLuZ5Jxnabkbb6cZALUkG5sdwKHlRgL7Ax6DA4kNh9nYpHkOHM7dODsBpOVA4jbcWhgb/xjUJc6Hakk3nJ3+gaCWZh4D5sQNUC0J8tI5BGw587CxWcbgcOLGw2zMlnMOpBlukM4pOJBggMcvx5MPNr75U5c473gb4403B2zk5Wenb/7wocJODpcWBgbGBlS+AVilAS7l2IB8A0Elo2AUjIJRMMIAAIdUZaAP6LLtAAAAAElFTkSuQmCC","orcid":"","institution":"East West University","correspondingAuthor":true,"prefix":"","firstName":"Md.Faishal","middleName":"Ahmed","lastName":"Rudro","suffix":""},{"id":540148902,"identity":"617deace-f6b2-4557-9fa2-ea2ebe7d28a9","order_by":1,"name":"Anzim Hasan Nabil","email":"","orcid":"","institution":"Govt. 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Media","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Suicide Ideation Detection, Ensemble Deep Learning, Explainable AI, CNN–LSTM, Sentiment Analysis, Mental 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Nevertheless, traditional deep learning models tend to neglect all at once emotional tone, linguistic subtlety, and contextual meaning. To address this deficiency, we introduce a novel multi-branch ensemble model combining CNN, BiLSTM, CNN\u0026ndash;LSTM, LightGBM, and XGBoost models by weighted soft-voting fusion strategy. The model combines semantic embeddings, TF\u0026ndash;IDF statistical features, and sentiment polarity features to facilitate deep contextual learning and explainable linguistic inference. Strong exploratory data analysis and statistical testing (t-tests, sentiment profiling, keyword frequency tests) substantiate significant emotional and compositional differences in suicidal and non-suicidal posts in the Kaggle Suicide and Depression Detection Dataset (232K Reddit posts). Experimental performance shows that the put forward ensemble outperforms all the baselines with test accuracy of 95.36% and AUC of 0.989. In addition, inclusion of Explainable AI (XAI) elements\u0026mdash;LIME and Attention-based visualizations\u0026mdash;ensures a clear explanation of feature importance and emotional attribution, facilitating enhanced clinical trust and interpretability. 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