Abstract
Mental disorder is an illness that are more common in technical employees and are increasing among working professionals. It is an important and challenging issue in the world as the stress among the technical employees are high due to work culture. Mental health in the healthcare is necessary to recognize individual’s situation and continue to predict diverse situations accurately. Therefore, this study proposes the mental health disorder prediction using various feature selection algorithms for a specific dataset called Tech survey Datasets. We applied multiple machine learning classification algorithms on the best features of RFE, RFECV and LASSO to obtain the performance metrics of the model and to decide the best accuracy, precision and recall of the respective models. There are 61 features in the Tech survey Dataset consisting of the data in the technical workplace worldwide that provides mental health attribute and frequency. The results are discussed and an aggregated table is developed using the performance metrics to understand the percentage of technical employees undergoes mental disorder. The proposed research evaluates various classification algorithms along with feature selection algorithm to forecast the mental disorder in a particular workplace and found to be 79% accuracy.
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Mallick, S., Panda, M. (2022). Application of Efficient Feature Selection and Machine Learning Algorithms in Mental Health Disorder Identification. In: Panda, M., et al. Innovations in Intelligent Computing and Communication. ICIICC 2022. Communications in Computer and Information Science, vol 1737. Springer, Cham. https://doi.org/10.1007/978-3-031-23233-6_26
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DOI: https://doi.org/10.1007/978-3-031-23233-6_26
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