Mental Stress Level Prediction and Classification based on Machine Learning

Stress is becoming an important factor in a person's life today. According to the World Health Organization, stress is a type of mental illness that affects the health of citizens. There is no one in the world who does not suffer from stress or depression. Everyone gets some amount of stress. Stress is a major symptom for mental health. Stress affects every aspect of a person's life such as emotions, thoughts, and behaviors. This paper presented the study on previous research on stress detection based on machine learning algorithms. presented a stress level classification framework using the PhysioBank dataset to analyze the stress level. The statistical analysis was used for feature selection and extraction, and it found that stress level classification has been successfully implemented based on the proposed gradient boost algorithm. The evaluated results showed that the proposed model achieved accuracy (83.33%), specificity (75%), Sensitivity (75%), Positive Predictive Value (90%), Negative Predictive Value (90%), Error Rate(16.66%), F1_Score (83.33%), Recall (75%). The proposed gradient boost algorithm performed well as compared to other machine learning algorithms, namely, KNN, Random Forest and, support vector machine. The proposed model was shown to be effective in classifying stress level prediction.