Stress Analysis Prediction for Coma Patient Using Machine Learning

Today’s working IT professionals frequently struggle with stress issues. The patient is now more likely to experience stress due to changing lifestyle and workplace cultures. Even while many companies and sectors deliver welfare programs and also make efforts to enhance work environments, the issue remains off from grasp. In this paper, we will utilize various techniques to analyze the stress patterns of working people and identify the variables that exert a massive effect on the system's distress. By utilizing machine learning techniques to forecast models to predict the threats of stressor encountered prognosis through using machine learning, we hope to streamline this procedure. In the working class, mental health illnesses linked to stress are not unusual. Concerns about the same have previously been highlighted by several researchers. In order to do this, information from coma patient answers from working medical experts’ mental health data was taken into consideration. After data extraction and preprocessing, various machine learning approaches were employed to supervise our framework. In this case, the mechanism can apply the machine learning techniques such as decision tree, random forest, KNN, logistic regression, and Naive Bayes. Results of the experiments demonstrated the system’s improved performance.