This paper is published in Volume-5, Issue-2, 2019
Area
Data Mining
Author
Pinninti Kusuma
Co-authors
Dokkari Siva
Org/Univ
Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India
Pub. Date
26 March, 2019
Paper ID
V5I2-1509
Publisher
Keywords
Data mining, Random forest, Generalized anxiety disorder, Knowledge

Citationsacebook

IEEE
Pinninti Kusuma, Dokkari Siva. A study on impact of stress levels among students using random forest algorithm, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Pinninti Kusuma, Dokkari Siva (2019). A study on impact of stress levels among students using random forest algorithm. International Journal of Advance Research, Ideas and Innovations in Technology, 5(2) www.IJARIIT.com.

MLA
Pinninti Kusuma, Dokkari Siva. "A study on impact of stress levels among students using random forest algorithm." International Journal of Advance Research, Ideas and Innovations in Technology 5.2 (2019). www.IJARIIT.com.

Abstract

Mental health presents one of the greatest and real challenges to the current generation of students. The competitive lifestyles cause them to manage different challenges to achieve their goals and people to be eager to achieve something, causing them to manage multiple frustrations and demands. In this pressurized and highly technical environment, anxiety disorders have slowly occupied their mental health. Nowadays Generalized Anxiety Disorder (GAD) is one of the mental health problems among students. It has been reported that about 2-5% of them. Here our raw data like questionnaire is a valuable resource, but it is underutilized at some circumstances. It could potentially be converted into useful and understandable knowledge via data mining technology. Data mining is capable to identify hidden patterns and relationships that can be used for predicting different disorders. For effective prediction, the random forest approach is one of the classification data mining techniques which produces good predictive characteristics. Therefore, this research adopts a random forest approach to predict different disorders like Generalized Disorder, Anxiety Disorder, Depression, and the accuracy of the predictive result is reported.