Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Satheesh, Mohith Manoharan; Nagavibha, R.; Chandy, Mishbah Pinto; Jayapandian, N. Title A name given to the resource Machine Learning Based Depression Prediction Using Gradient Boosting Algorithm Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Communications in Computer and Information Science;Volume;2621 CCIS;pp.21-32 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-00793-3_3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-00793-3_3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015604916?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015604916?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Satheesh M.M., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Nagavibha R., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Chandy M.P., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Jayapandian N., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India Description An account of the resource Depression is one of the major diseases, more than one million people are facing this issue. To achieve the best results possible, it is essential to monitor and intervene when needed regularly. While there are many ways to observe the mental well-being of an individual in a workplace environment, AI has the potential to enhance the accuracy, efficiency, and speed when it comes to diagnosing any issues. This study focuses on developing an ML system for distinguishing symptoms of depression among individuals in the workplace. The dataset comprises detailed information on the signs and symptoms of depression among individuals, it particularly focuses on the observed negative consequences at the workplace, physical health issues and their negative consequences, treatment. In this experimental process two main machine learning algorithms were used, the Random Forest and Gradient Boosting algorithm. Both the algorithms have an overall accuracy of 82%, but based on maximization of the overall performance, the Gradient Boosting model is slightly better than the Random Forest. Furthermore, our exploration demonstrates overall performance like character fashions, signaling promising prospects for sturdy and correct depression analysis class systems. This study highlights the power of machine learning that could revolutionize depression care by identifying mental health problems early. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Artificial Intelligence; Decision Tree; Depression; Gradient Boosting; Healthcare; Machine Learning; Random Forest Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-303200792-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online