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Title: :  Comparative Analysis of Machine Learning Techniques for Human Stress Level Prediction: Performance Metrics and Insights
PaperId: :  24890
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 4 2024
DUI:    16.0415/IJARIIE-24890
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
A. RasheedhaSri Ramakrishna Engineering College
L. ShruthikaSri Ramakrishna Engineering College

Abstract

Biomedical Engineering
Machine Learning, Stress Prediction, Random Forest, Support Vector Machine, Long Short-Term Memory
In this review study, several machine learning models that are used to predict human stress levels are thoroughly analyzed, with a particular emphasis on Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks. The design, benefits, limitations, and performance measures of each model are thoroughly examined to provide a comprehensive understanding of their capabilities. Known for its ensemble learning methodology, Random Forest constructs several decision trees to improve precision and robustness. Even though its astounding accuracy rates, which range from 72% to 95%, can be challenging to interpret and computationally demanding, especially in vital industries like healthcare. However, with accuracy rates ranging from 79% to 95%, SVM is acknowledged for its efficiency in managing high-dimensional spaces and complicated datasets. Nevertheless, it is sensitive to feature scaling and can yield difficult-to-interpret complex decision limits. On the other hand, LSTM networks are especially well-suited for stress prediction from time-series data since they are expressly made for sequence prediction tasks and are excellent at capturing temporal relationships in data. LSTM models have proven to be remarkably effective, with accuracy reaching 99.71%. However, their usefulness in real-time scenarios may be limited because to their requirement for substantial processing resources and a huge volume of labeled data for effective training. All in all, these models' performance measures highlight how successful they are at predicting stress, with RF, SVM, and LSTM each having particular advantages and disadvantages. This study offers insightful information for academics and practitioners in the subject of stress analysis, emphasizing the significance of choosing the right model depending on particular requirements.

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IJARIIE A. Rasheedha, and L. Shruthika. "Comparative Analysis of Machine Learning Techniques for Human Stress Level Prediction: Performance Metrics and Insights" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 4 2024 Page 3346-3350
MLA A. Rasheedha, and L. Shruthika. "Comparative Analysis of Machine Learning Techniques for Human Stress Level Prediction: Performance Metrics and Insights." International Journal Of Advance Research And Innovative Ideas In Education 10.4(2024) : 3346-3350.
APA A. Rasheedha, & L. Shruthika. (2024). Comparative Analysis of Machine Learning Techniques for Human Stress Level Prediction: Performance Metrics and Insights. International Journal Of Advance Research And Innovative Ideas In Education, 10(4), 3346-3350.
Chicago A. Rasheedha, and L. Shruthika. "Comparative Analysis of Machine Learning Techniques for Human Stress Level Prediction: Performance Metrics and Insights." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 4 (2024) : 3346-3350.
Oxford A. Rasheedha, and L. Shruthika. 'Comparative Analysis of Machine Learning Techniques for Human Stress Level Prediction: Performance Metrics and Insights', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 4, 2024, p. 3346-3350. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Comparative_Analysis_of_Machine_Learning_Techniques_for_Human_Stress_Level_Prediction__Performance_Metrics_and_Insights_ijariie24890.pdf (Accessed : ).
Harvard A. Rasheedha, and L. Shruthika. (2024) 'Comparative Analysis of Machine Learning Techniques for Human Stress Level Prediction: Performance Metrics and Insights', International Journal Of Advance Research And Innovative Ideas In Education, 10(4), pp. 3346-3350IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Comparative_Analysis_of_Machine_Learning_Techniques_for_Human_Stress_Level_Prediction__Performance_Metrics_and_Insights_ijariie24890.pdf (Accessed : )
IEEE A. Rasheedha, and L. Shruthika, "Comparative Analysis of Machine Learning Techniques for Human Stress Level Prediction: Performance Metrics and Insights," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 4, pp. 3346-3350, Jul-Aug 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Comparative_Analysis_of_Machine_Learning_Techniques_for_Human_Stress_Level_Prediction__Performance_Metrics_and_Insights_ijariie24890.pdf [Accessed : ].
Turabian A. Rasheedha, and L. Shruthika. "Comparative Analysis of Machine Learning Techniques for Human Stress Level Prediction: Performance Metrics and Insights." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 4 ().
Vancouver A. Rasheedha, and L. Shruthika. Comparative Analysis of Machine Learning Techniques for Human Stress Level Prediction: Performance Metrics and Insights. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(4) : 3346-3350. Available from: https://ijariie.com/AdminUploadPdf/Comparative_Analysis_of_Machine_Learning_Techniques_for_Human_Stress_Level_Prediction__Performance_Metrics_and_Insights_ijariie24890.pdf
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