ItemConference Paper

Machine Learning Based Depression Prediction Using Gradient Boosting Algorithm

Title
Machine Learning Based Depression Prediction Using Gradient Boosting Algorithm
Creator
Satheesh, Mohith Manoharan; Nagavibha, R.; Chandy, Mishbah Pinto; Jayapandian, N.
Description
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.
Source
Communications in Computer and Information Science;Volume;2621 CCIS;pp.21-32
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Artificial Intelligence; Decision Tree; Depression; Gradient Boosting; Healthcare; Machine Learning; Random Forest
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 18650929; ISBN: 978-303200792-6;
Format
online
Language
English
Type
Conference paper

Citation

Satheesh, Mohith Manoharan; Nagavibha, R.; Chandy, Mishbah Pinto; Jayapandian, N., “Machine Learning Based Depression Prediction Using Gradient Boosting Algorithm,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25338.

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