Prediction of mental illness using ensemble model and grid search hyperparameter optimization

Abstract

The early prediction of mental illnesses reduces the severity of the disease. The symptoms like poor concentration, unstable energy of the body, pressure, and loss of interest cause depression. A large-scale group decision making (LSGDM) method is proposed in this paper along with the ensemble classifier model by combining convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM) for effective classification of depression, anxiety and stress (DAS) levels. The data is collected from the depression anxiety stress scale-42 (DASS-42) dataset for efficient classification and predictions of mental health problems. The min-max normalization is used to pre-process the data, and the feature selection is done for extracting informative features. The extracted features are provided as input to the ensemble classifier. The proposed LSGDM model maximizes the classification accuracy with the help of grid search CV hyperparameter tuning, and results in an accuracy of 98.88%, precision of 98.21%, recall of 99.62%, F1-Score of 98.90%, and MCC of 99.41%. The proposed LSGDM method gives superior results when compared to the existing machine learning (ML) based ensemble model, a principal component analysis along with modified fast correlation based filtering (PCA-mFCBF), and LSTM based RNN (LSTM- RNN).