Abstract:
Stress has become a major risk factor in today’s world, which affects different people from
different demographics. This research uses a large dataset with various physiological and
behavioral factors to present a novel method for detecting stress. This study will analyze
11 different variables, such as physical activity level, gender, age, sleep quality, and
cardiovascular health markers etc. to build a robust model that can reliably detect and
predict stress levels. My goal in this study is to create reliable models for stress detection
through the analysis of several features and the use of cutting-edge machine learning
methods. Stress is a major public health issue that has a big impact on people’s wellbeing.
Because subjective self-reporting is frequently used in traditional stress assessment
methods, precise and objective measurement methods are crucial. In order to precisely
identify, anticipate, and forecast stress levels based on a variety of characteristics, such as
physiological, behavioral, and contextual aspects, I suggest a data-driven method. I
investigate the efficiency of various machine learning models in identifying intricate
patterns and relationships in the data, such as Decision Tree, Random Forests, and Gradient
Boosting Machines (GBM). To extract pertinent data and improve model performance,
feature engineering and selection strategies are used. I determine the most useful features
for stress detection and evaluate each model’s predictive ability through rigorous testing
and cross-validation. In addition, I examine the underlying trends and connections between
the stressors and traits that have been revealed, offering important new understandings into
the variables affecting stress levels. These realizations can direct the creation of customized
treatments that are suited to each person’s particular requirements, enabling efficient stress
management techniques. This study adds to the body of knowledge in the field of stress
detection and mitigation by bridging the theoretical and practical divide. This study adds
to the body of knowledge in the field of stress detection and mitigation by bridging the
theoretical and practical divide. In this work, my accuracy rate is 95%.
