Call for Papers:Vol.11 Issue.3

Submission
Last date		 28-Jun-2025
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-11,Issue-3. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

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.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

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
BibTex EndNote RefMan RefWorks

Number Of Downloads



Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
TEACHING WITH AI: LIVED EXPERIENCES, RESPONSIBILITIES, AND CHALLENGES OF TECH-TECHERSEducationJaneth M. Wariza Download
USER’S PERSPECTIVES ON AI- POWERED VIRTUAL ASSISTANT IN EDUCATION AND EVERYDAY LIFEEducational TechnologyRechelle M. Cardines Download
FLAP-AI BIRDArtificial Intelligence and Machine LearningRamya BN Download
AL-Powered healthcare chatbot for personalized medical assistance and wound classificationAI-Powered Healthcare SystemProf. Manjunath N Download
AI-Powered Resume Generation: A Streamlit-Based System Using PyTorch for Intelligent Career ProfilingComputer EngineeringAditi Meshram Download
AI based object color detection and sorting automationRobotics and AutomationBHUVAN THEJASWI T Download
Advanced knowledge on Synthetic Data Generation Using GANs for Medical Imaging ApplicationsArtificial IntelligenceRajeev Ranjan Download
Information on Cognitive AI Assistants for Visually Impaired Navigation SupportComputer ScienceNagaraj.H Download
Speech Emotion AI for Mental Health Monitoring in Call CentersComputer ScienceGowtham Kumar Download
Multi-Agent Reinforcement Learning for Coordinated Drone SwarmsComputer ScienceDeepak Gowda Download
AI-Based Soil Health Monitoring Using Remote Sensing and Deep LearningArtificial IntelligenceChethan Nagraj Download
Dynamic Criminal Network Link Forecasting via Deep Reinforcement LearningDeep LearningSrikant Tayade Download
Virtual Try-On-Enhancing Fashion Exploration for Gen-ZDeep Learning & Generative ModelsAnushka Mane Download
To Develop an EMG-Based Expert System for the Detection of Wrist Flexion and ExtensionArtificial Intelligence and Data ScienceRohit Mehta Download
LUNG CANCER PREDICTION AND DETECTION USING MLCOMPUTER SCIENCE & ENGINEERINGRASHMI T N Download
12