Abstract
Mental health has become an important aspect of the daily lives of people these days. As days are becoming hectic, work pressure on them increases, and as a result, people suffer from various mental health conditions. Depression is one such mental health condition, which is affecting the youths at large. Our aim is to provide a machine learning approach so as to detect the signs of mental health conditions so that the victims can be treated as soon as possible. The main machine learning model uses 9 algorithms, k nearest neighbor, Logistic regression, Decision tree, Stacking, Random forest, Bagging, Boosting, Xgboost, SVM, and compared for their accuracy. The feature selection method is used to identify the eight most important and correlate attributes from the 27 attributes identified from the text. Classifiers were made to act on the eight attributes and ROC curves were generated in order to predict their accuracy. This would go a long way in diagnosing and treating patients for much healthier well-being. Every life is meaningful. There is no shame in admitting someone suffers from mental health conditions and should immediately consult an expert to overcome their problems.
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Nag, A., Bandyopadhyay, A., Nayak, T., Banerjee, S., Panda, B., Mishra, S. (2024). Distinguishing the Symptoms of Depression and Associated Symptoms by Using Machine Learning Approach. In: Verma, O.P., Wang, L., Kumar, R., Yadav, A. (eds) Machine Intelligence for Research and Innovations. MAiTRI 2023. Lecture Notes in Networks and Systems, vol 832. Springer, Singapore. https://doi.org/10.1007/978-981-99-8129-8_8
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