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

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

rajeshkanna_r

Contact

Rajeshkanna R

Contact

T. Mohana Priya

Contact

Dr. M. Punithavalli

Contact

Dr. R. Rajesh Kanna

Contact

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

Article Fingerprint

ReserarchID

CSTSDE9L8FO

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

AI TAKEAWAY

Connecting with the Eternal Ground

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.

References

23 Cites in Article

Reference Format

Kiyong Noh,Heon Lee,Ho-Sun Shon,Bum Lee,Keun Ryu (2006). Associative Classification Approach for Diagnosing Cardiovascular Disease.
Heon Lee,Ki Noh,Keun Ryu (2007). Mining Biosignal Data: Coronary Artery Disease Diagnosis Using Linear and Nonlinear Features of HRV.
Niti Guru,Anil Dahiya (2007). Decision Support System for Heart Disease Diagnosis Using Neural Network.
Hai Wang Medical Knowledge Acquisition through Data Mining.
Sellappan Palaniappan,Rafiah Awang (2008). Intelligent heart disease prediction system using data mining techniques.
R Lathaparthiban,Subramanian (2008). Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm.
S Chaitrali,Dangare,S Sulabha,Apte (2012). Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques.
S Vijiyarani (0975). An Efficient Classification Tree Technique for Heart Disease Prediction.
Latha Parthiban,R Subramanian (2008). An intelligent agent for detection of erythemato- squamous diseases using Co-active Neuro-Fuzzy Inference System and genetic algorithm.
Martin Gjoreski,Monika Simjanoska,Anton Gradisek,Ana Peterlin,Matjaz Gams,Gregor Poglajen (2017). Chronic Heart Failure Detection from Heart Sounds Using a Stack of Machine-Learning Classifiers.
Muhammad Ketutagungenriko,Dadanggunawan Suryanegara,Al (2018). Heart Disease Diagnosis System with k-Nearest Neighbors Method Using Real Clinical Medical Records.
Mustapha Abdallahkassem,Chady Hamad,Eliefayad Moucary (2016). A Smart Device for the Detection of Heart Abnormality using R-R Interval.
Rima Madhurapatil,Jadhav,Vishakhapatil,Geetachillarge Aditibhawar (2019). Prediction and Analysis of Heart Disease Using SVM Algorithm.
Sheik Abdullah,R Rajalaxmi (2012). A Data mining Model for Predicting the Coronary Heart Disease Using Random Forest Classifier.
Jagdeep Singh,Amit Kamra,Harbhag Singh (2016). Prediction of heart diseases using associative classification.
Ridairfan Tahiramahboob,Bazelahghaffar (2017). Evaluating Ensemble Prediction of Coronary Heart Disease using Receiver Operating Characteristics.
V Balasundar,T Devi,N Saravan (2012). Development of a Data Clustering Algorithm for Predicting Heart.
D Sayali,H Jadhav,Channe (2016). Comparative Study of KNN, Naive Bayes and Decision Tree Classification Techniques.
Chandrasekhar Sai,Reddy,Jaya Puneetpalagi (2016). Heart Disease Prediction Using ANN Algorithm in Data Mining.
Asil Dursundelen,Leman Oztekin,Tomak (2012). An analytic approach to better understanding and management of coronary surgeries.
L David,Dursun Olson,Yanyanmeng Delen (2012). Comparative analysis of data mining methods for bankruptcy prediction.
Akhilesh Kumar Yadav,Divyatomar,Sonali Agarwal (2013). Clustering of Lung Cancer Data Using Foggy K-Means.
Supriya Reshma,Kinariwala (2019). Network Malware Detection using Soft Computing and Machine Learning Techniques.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite 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).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 20 Issue C2

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-C Classification C.1.2

Submission ReceivedDecember 6, 2019
Peer Review Double Blind
Handling Editor
Accepted January 4, 2020
Published January 15, 2020

Version of record

v1.2

Issue date

December 21, 2020

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Views: 4401

Total Downloads: 951

2026 Trends

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]