Depression has become a common issue among IT industry professionals today. Lifestyle changes and new work culture increase the risk of depression among employees. Various companies and organizations offer mental health plans and try to pacify the work environment. However, the problem is already out of control. This research paper proposes an effective deep learning model for stress prediction among working employees with the help of lion optimization-based Optimal Artificial Neural Network (OANN) model. Here, the features are selected using optimal ANN technique and the diseases are predicted using lion optimization method. ANN technique eliminates inappropriate and unnecessary attributes in a significant manner, once the information on calculated characteristics and weight is disseminated to lion optimization classifier. The test results inferred that the Artificial Neural Network is highly efficient than the current OANN algorithm method, based on lion optimization. The study evaluated the data and found that the performance of employees working under normal conditions was higher when compared to the performance of employees who work under stress. Furthermore, attitude-coping efforts may be a cognitive-behavioral mechanism, which explains how workload is related to courage and work performance of employees with high stress level.

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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.

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