https://www.ijhbs.com/index.php/ijhbs <p style="text-align: justify;"><strong>International Journal of Health and Biological Sciences (IJHBS) </strong>is an open-access; freely accessible, online and print quarterly peer-reviewed international journal publishes a wide spectrum of advanced research on all medical specialties including ethical and social issues. IJHBS is a gateway to enlighten the latest research/issues happening all around the world of medical and health sciences.</p> <p style="text-align: justify;">The journal publishes original research articles in the form of full-length papers or short communications especially those with multidisciplinary nature. The journal welcomes review articles, mini-reviews, case reports, letter to the editor, guest editorial or commentaries.</p> IJHBS Publication en-US International Journal of Health and Biological Sciences 2590-3365 <p>Author(s) hold the copyright and retain publishing rights without restrictions.&nbsp;</p> <p><img src="/public/site/images/ramandeepadmin/88x312.png">&nbsp;This work is licensed under a&nbsp;<a href="http://creativecommons.org/licenses/by/4.0/" rel="license">Creative Commons Attribution 4.0 International License</a>.</p> https://www.ijhbs.com/index.php/ijhbs/article/view/56 <p>Youth suicidality remains a critical global mental health challenge, necessitating innovative and data-driven approaches to early detection and intervention. This study examines the emerging role of digital phenotyping and sensor-based risk identification systems in assessing suicidality trends among young populations. By leveraging data from smartphones, wearable devices, and online behavioral patterns, digital phenotyping enables continuous, real-time monitoring of psychological states, including mood variability, social withdrawal, and sleep disturbances.<br><br>Sensor-based systems further enhance predictive capacity through the integration of machine learning algorithms capable of identifying subtle behavioral anomalies associated with suicidal ideation.<br><br>The research adopts a multidisciplinary framework, combining insights from computational psychiatry, behavioral science, and artificial intelligence to evaluate the effectiveness, limitations, and ethical implications of these technologies. While findings suggest significant potential for early risk detection and personalized intervention, concerns regarding data privacy, algorithmic bias, and informed consent remain paramount. The study concludes by highlighting the need for ethically grounded, clinically integrated, and policy-supported implementations to ensure responsible deployment in youth mental health contexts.</p> Kaliyat Gamba John Emoabino Copyright (c) 2026 Kaliyat Gamba, John Emoabino http://creativecommons.org/licenses/by/4.0 2023-04-25 2023-04-25 6 4 1 24