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In this research, we focus specifically on mental disorder which is bipolar disorder using ma­chine learning techniques, utilizing a simple dataset from Kaggle for training and evaluation. The study involves applying various ML models, including R. Forest, XG-Boost, and S. Vector Machines (SVM), on the dataset. We assess the performance of these models using evaluation MSE (how far actual value to predicted value), Precision, and Fl Score to determine their effectiveness in predicting bipolar disorder. However, through extensive experimentation, we found that the combination of (CNN) and (LSTM) networks outperformed the other algorithms, achieving an overall accuracy of 95%.

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Survey on Prediction of Bipolar Disorder Using CNN and LSTM

  • 会议论文

  • 发表日期：

    2026年

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  • Ankita Mehta,
  • Shailesh Gahane

摘要

In this research, we focus specifically on mental disorder which is bipolar disorder using ma­chine learning techniques, utilizing a simple dataset from Kaggle for training and evaluation. The study involves applying various ML models, including R. Forest, XG-Boost, and S. Vector Machines (SVM), on the dataset. We assess the performance of these models using evaluation MSE (how far actual value to predicted value), Precision, and Fl Score to determine their effectiveness in predicting bipolar disorder. However, through extensive experimentation, we found that the combination of (CNN) and (LSTM) networks outperformed the other algorithms, achieving an overall accuracy of 95%.