Key result
Support Vector Machine (SVM) algorithms produced high accuracy for stress detection when compared with other machine learning classifiers.
Why the study?
Stress is a major issue that can cause physiological and psychological problems, prompting researchers to propose various detection models.
Support Vector Machine (SVM) algorithms appear to provide high accuracy for stress detection compared to other machine learning classifiers.
May aid stress detection via SVM; leaves open prospective validation before clinical use.
Now-a-days stress is one of the major issues in every individual’s life. It may cause many physiological and psychological problems. Many researchers have taken this into account and proposed various stress detection models. This paper mainly focuses on various stress detection models which are published in the latest years. The stress level of an individual is classified using machine learning algorithms like K-Nearest Neighbor (KNN), Naive Bayes (NB), Logistic regression (LR), Support Vector Machine (SVM) etc. It is observed that SVM produces a high accuracy when compared with other classifiers. The organization of the paper is as follows, Section I gives a brief introduction on stress and a basic idea about the datasets used by the researchers. Section II provides literature review. Section III contains analysis on collected literature.
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A et al. (2023) conducted a review in Stress. Machine learning algorithms (e.g., SVM, KNN, NB, LR) vs. Other classifiers was evaluated on Accuracy of stress detection. Support Vector Machine (SVM) algorithms produced high accuracy for stress detection when compared with other machine learning classifiers.
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