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The human brain and stress are closely connected, and understanding this relationship is crucial in neurology. By using advanced machine-learning (ML) techniques, this research aims to uncover how neurological symptoms can help predict brain diseases. The data for this study is considered from the HOPE CARE mental health clinic. Sophisticated computer algorithms such as Support Vector Machine (SVM), Gaussian Naive Bayes, and Random Forest classifier were used to analyze the patterns in neurological symptoms, which could provide the way for better disease management. The preliminary results of our research are promising, with the Random Forest classifier achieving an impressive 97% accuracy in predicting brain diseases. This research represents a significant advancement in the field of neurology, with the potential to enable early detection and personalized treatment of brain diseases.

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Brain Diseases Prediction Using ML

  • 会议论文

  • 发表日期：

    2025年

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  • Tanishq Sharma,
  • Dhruv Raheja,
  • Rani Lathwal

摘要

The human brain and stress are closely connected, and understanding this relationship is crucial in neurology. By using advanced machine-learning (ML) techniques, this research aims to uncover how neurological symptoms can help predict brain diseases. The data for this study is considered from the HOPE CARE mental health clinic. Sophisticated computer algorithms such as Support Vector Machine (SVM), Gaussian Naive Bayes, and Random Forest classifier were used to analyze the patterns in neurological symptoms, which could provide the way for better disease management. The preliminary results of our research are promising, with the Random Forest classifier achieving an impressive 97% accuracy in predicting brain diseases. This research represents a significant advancement in the field of neurology, with the potential to enable early detection and personalized treatment of brain diseases.