Paper Key : IRJ************116
Author: S Bharani,M Kamaleesh
Date Published: 18 Oct 2023
Abstract
A vital aspect of promoting mental health and mitigating its negative impacts on people is properly and effectively identifying mental stress. The Support Vector Machine (SVM) and Random Forest (RF), two effective machine learning techniques, are used in this abstract's introduction to a study to identify mental stress. The objective of the study is to use the capacities of these algorithms to recognise physiological and behavioural indicators of mental stress. Wearable technology and self-report surveys are just two of the sources of the study's broad dataset.The collection includes behavioural information gathered from smartphone applications as well as physiological indications including heart rate variability, skin conductance, and body temperature. Standardisation and preprocessing procedures are used to get the raw data ready for feature extraction and analysis. Based on the collected features, individuals are divided into stressed and non-stressed categories using the Support Vector Machine and Random Forest algorithms. The SVM is used because it can identify the best hyperplanes to divide classes, but the Random Forest approach is excellent at managing intricate data interactions.Keywords : Support Vector Machine, Random Forest, Mental Stress, Machine Learning