Integrating Physiological Data with Machine Learning for Early Detection of Stress in Patients.

Abstract

Stress is an important physiological and psychological disorder that affects human health and wellness in a tremendous way. As the demand to have effective and efficient stress detection systems has been on the rise, there is a potential in the integration of physiological data with sophisticated machine learning algorithms. The paper will suggest a hybrid approach that uses a mix of Convolutional Neural Networks (CNNs) and Random Forests (RF) to interpret physiological data to indicate the true presence of stress at real-time. The model is based on major physiological indicators such as heart rate variability (HRV), electrodermal activity (EDA), and respiration rate to categorize stress and non-stress situations. The algorithm includes processing original physiological data to extract features (via CNNs) which can learn hierarchical patterns in the data. The features are then extracted and fed to an RF classifier which is known to be capable of dealing with complex datasets and give robust and accurate classification. The optimization of the hyperparameters is done with the help of the Grid Search or the Random Search in order to optimize the parameters of the model, making them more efficient. A series of physiological data is used as a benchmark to evaluate the performance of the model in terms of accuracy, precision, recall, and F1-score. The experimental findings reveal that CNN-RF hybrid model is better than the classical machine learning classifiers as it has high accuracy and stability in stress detection under different conditions. It is also characterized by high-performance in generalization as the model is able to perform well when unseen physiological data are used. The study will enhance the development of smart stress monitoring systems as it will integrate physiological data with the latest machine learning approaches. The proposed model can be used in the sphere of healthcare, working environment, and personal health. Current research will be expanded by working on the real-time application of this system within wearable devices, which will allow maintaining humanity-unobtrusive monitoring of stress and introduce adaptive measures to reduce stress.