Machine Learning Algorithm for Development of Enhanced Support Vector Machine Technique to Predict Stress

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rajeshkanna_r
rajeshkanna_r
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Rajeshkanna R
Rajeshkanna R
3
T. Mohana Priya
T. Mohana Priya
4
Dr. M. Punithavalli
Dr. M. Punithavalli
5
Dr. R. Rajesh Kanna
Dr. R. Rajesh Kanna

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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.

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No external funding was declared for this work.

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The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

rajeshkanna_r. 2020. \u201cMachine Learning Algorithm for Development of Enhanced Support Vector Machine Technique to Predict Stress\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 20 (GJCST Volume 20 Issue C2): .

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GJCST Volume 20 Issue C2
Pg. 63- 72
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-C Classification: C.1.2
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December 21, 2020

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English

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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.

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Machine Learning Algorithm for Development of Enhanced Support Vector Machine Technique to Predict Stress

Rajeshkanna R
Rajeshkanna R
T. Mohana Priya
T. Mohana Priya
Dr. M. Punithavalli
Dr. M. Punithavalli
Dr. R. Rajesh Kanna
Dr. R. Rajesh Kanna

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