Abstract: Healthcare is a core of human’s life. Being healthy is one of the main objectives of life ever since BC. Health is maintained and improved by the lifestyles, social, happiness and even willingness to live. Predicting a person's mood tomorrow, from data collected unobtrusively using wearable sensors and smartphones, could have a number of beneficial clinical applications; While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional Machine Learning (ML) methods frequently yield low performance in this domain. An integrated system to make human lives more easily and to help people in terms of mental health. The main objective of this project is to classify a new data and inform whether a person with stress is affected from acute stress or chronic stress. We will use the heart rate data taken for months and analyzes the data and find the Heart rate variance that constantly related with the stress. After that a variable is found, which is used as an input to the ensemble classifier which includes the Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) approaches. The best classifier will be selected by voting process.

PDF | DOI: 10.17148/IJARCCE.2023.12543

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