A novel feature selection with stochastic gradient descent logistic regression for multilabeled stress prediction in working employees

In the recent times, stress prediction becomes a hot research area and several research works have been developed to address it. The advent of machine learning (ML) models assists the stress prediction process to understand the patterns effectively and offer effective perceptions about possible future intervention. In this view, this article presents a multi-labeled stress prediction in working employee using extremely randomized tree (ET) based feature selection (FS) and stochastic gradient descent (SGD) with logistic regression (LR), called ETSGD-LR model. First, the ET based FS technique can be used to compute impurity-based feature importance, which in turn can be used to discard irrelevant features. In addition, the SGD-LR model is used to classify the feature reduced subset into different class labels. For experimental validation, we have collected our own stress prediction dataset with 1197 records of employees collected from schools, banks, universities, and so forth from different institutions. Among them 1197 records are filtered with various diseases and work pressure. A detailed set of simulations were carried out in Python Programming tool, and the results are analyzed in terms of sensitivity, specificity, accuracy, precision, F-score, and kappa. The obtained simulation outcome ensured the superior performance of the ETSGD-LR model over the compared methods with the maximum sensitivity, specificity, and accuracy of 0.980, 0.900, and 0.972, respectively. The experimental results shown that the inclusion of FS process helps to improve the classification performance.