Scholarlink

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.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine Learning Based Depression Prediction Using Gradient Boosting Algorithm

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Mohith Manoharan Satheesh,
  • R. Nagavibha,
  • Mishbah Pinto Chandy,
  • N. Jayapandian

摘要

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.