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In the contemporary, rapidly evolving environment, professionals across diverse industries exert considerable effort to remain informed about the latest advancements, especially in fast-moving sectors. The relentless demand to stay competitive and knowledgeable can adversely affect mental well-being, resulting in various psychological challenges. Depression is one of the most prevalent and worrying mental health concerns. A person’s quality of life can be severely impacted by this particular mental health condition. Recognizing the gravity of this issue, experts have focused on identifying the main causes of depressive symptoms in professionals. Analysis utilizing machine learning algorithms was the major goal to properly identify depression and related diseases. The study analyzed and interpreted data using Random Forest, SVM, XGBoost, and Logistic Regression. Random Forest has the greatest accuracy rate of 81.2% because of its ensemble method and ability to handle complicated, high-dimensional input. The study shows how machine learning may help us understand mental health concerns like bipolar disorder and depression by allowing early identification and treatment.

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Psychological Instability Identification Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • Yash Chauhan,
  • Shilpi Mohanty,
  • Sunil Kumar,
  • Dharmendra Singh Rajput

摘要

In the contemporary, rapidly evolving environment, professionals across diverse industries exert considerable effort to remain informed about the latest advancements, especially in fast-moving sectors. The relentless demand to stay competitive and knowledgeable can adversely affect mental well-being, resulting in various psychological challenges. Depression is one of the most prevalent and worrying mental health concerns. A person’s quality of life can be severely impacted by this particular mental health condition. Recognizing the gravity of this issue, experts have focused on identifying the main causes of depressive symptoms in professionals. Analysis utilizing machine learning algorithms was the major goal to properly identify depression and related diseases. The study analyzed and interpreted data using Random Forest, SVM, XGBoost, and Logistic Regression. Random Forest has the greatest accuracy rate of 81.2% because of its ensemble method and ability to handle complicated, high-dimensional input. The study shows how machine learning may help us understand mental health concerns like bipolar disorder and depression by allowing early identification and treatment.