Prediction and Analysis of Multiple Causes of Mental Health Problems Based on Machine Learning

To prevent other types of mental health problems from being misclassified as depression, as well as to remedy the problem of inadequate resources for mental health consultations. This study first analyzes the types of different causes of mental health problems, providing an important basis for better understanding the diversity and complexity of this field. Subsequently, a machine learning approach was used to predict the potential causes of different types of mental health problems. This research provides new perspectives and methods for early identification and personalized treatment of mental health problems. The experimental results show that depression accounts for only 16.9% of mental health problems. In the prediction of the causes of mental health problems, the SVM method performed best in predicting the causes of mental health problems, outperforming 5 machine learning methods and 3 deep learning methods. Through these studies, we hope to prevent other types of mental health problems from being misclassified as depression and to remedy the lack of resources for mental health counseling. This will help increase the success rate of early intervention and provide better mental health support for patients.