  Stress, anxiety, and nervousness are all high-risk health states in everyday
life. Previously, stress levels were determined by speaking with people and
gaining insight into what they had experienced recently or in the past.
Typically, stress is caused by an incidence that occurred a long time ago, but
sometimes it is triggered by unknown factors. This is a challenging and complex
task, but recent research advances have provided numerous opportunities to
automate it. The fundamental features of most of these techniques are electro
dermal activity (EDA) and heart rate values (HRV). We utilized an accelerometer
to measure body motions to solve this challenge. The proposed novel method
employs a test that measures a subject's electrocardiogram (ECG), galvanic skin
values (GSV), HRV values, and body movements in order to provide a low-cost and
time-saving solution for detecting stress lifestyle disease in modern times
using cyber physical systems. This study provides a new hybrid model for
lifestyle disease classification that decreases execution time while picking
the best collection of characteristics and increases classification accuracy.
The developed approach is capable of dealing with the class imbalance problem
by using WESAD (wearable stress and affect dataset) dataset. The new model uses
the Grid search (GS) method to select an optimized set of hyper parameters, and
it uses a combination of the Correlation coefficient based Recursive feature
elimination (CoC-RFE) method for optimal feature selection and gradient
boosting as an estimator to classify the dataset, which achieves high accuracy
and helps to provide smart, accurate, and high-quality healthcare systems. To
demonstrate the validity and utility of the proposed methodology, its
performance is compared to those of other well-established machine learning
models.
