Authors : Vinutha D; Dr. Nirmala S

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://tinyurl.com/2mx8fmk6

Scribd : https://tinyurl.com/ycdac757

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG1351

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Stress, often known as stressors, is a psychological or emotional state brought on by difficult or inevitable situations. Understanding human stress levels is vital to preventing negative life experiences. There may be connections between sleep-related difficulties and a range of psychological, social, and medical conditions. The aim is to look into the empirical identification of human stress levels by applying algorithmic techniques with health data. After data pre- processing, a few algorithmic approaches were utilized to assess stress levels, which were categorized from low to high: Multilayer Perception, Random Forest, Support Vector Machine, Decision Trees, Na ̈ıve Bayes, and Logistic Regression. This strategy made it possible to compare methods and find the most precised one.

Keywords : Naive Bayes Classifier, SVM, AdaBoost Classi- Fier, Stress Predictor, Data Analysis, MLP Classifier.

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