Abstract

Stress is a common risk factor for many diseases. A correct and efficient prediction model is required to predict stress levels for targeted prevention and intervention in the personal healthcare domain. Before preventing the event of stress-related diseases, stress should be detected and managed early. However, surveys are used to evaluate an individual's stress condition with ease of measurement and requiring little time. However, anything that puts high demands on a person makes it stressful. This includes positive events such as getting married, buying a house, going to college, or receiving a promotion. Of course, not all stress is caused by external factors. Stress can also be internal or self-generated, when a person worries excessively about something that may or may not happen, or have irrational, pessimistic thoughts about life. This article aims to develop a predictive model to find the interruption of stress using an efficient way. One of the successive machine learning algorithm is SVM. This paper proposed to enhance the parameters of SVM which is used to improve the efficiency for predicting stress. This article proposed an Enhanced Support Vector Machine classifier to predict Stress. The stress dataset is downloaded from the Kaggle repository with 951 instances and 21 attributes.

How to Cite
R, T. MOHANA PRIYA, DR. M. PUNITHAVALLI, DR. R. RAJESH KANNA, Rajeshkanna. Machine Learning Algorithm for Development of Enhanced Support Vector Machine Technique to Predict Stress. Global Journal of Computer Science and Technology, [S.l.], dec. 2020. ISSN 0975-4172. Available at: <https://computerresearch.org/index.php/computer/article/view/1990>. Date accessed: 23 jan. 2022.