Prediction of stress level based on physical activity using machine learning

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%.