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A Machine Learning Framework to Predict Depression, Anxiety and Stress

Yesudas, Antony (2022) A Machine Learning Framework to Predict Depression, Anxiety and Stress. Masters thesis, Dublin, National College of Ireland.

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Many people have experienced psychological health problems like stress, anxiety, and depression, over the previous few decades. It is essential to identify mental health conditions quickly and treat them before they worsen. Some people do, however, admit that they struggle with mental illnesses, including stress, anxiety, and depression. Contrarily, the majority of people consider it to be mood changes. Several studies have been done to determine whether user posts on social media can identify mental diseases like depression, anxiety, and stress. This paper predicts psychological issues like stress, anxiety, and depression. Also examined are five distinct severity levels. Random Forest, Naive Bayes and a Neural networks model were used in this research. Data is collected from the publicly available DASS42 tool to apply these algorithms. Random Forest and Naive Bayes performed well for Depression, Anxiety and stress with an average accuracy of 95%, but using evaluation metrics; the Neural networks model was chosen as the best accuracy model with an accuracy of 99%.

Item Type: Thesis (Masters)
Uncontrolled Keywords: DASS42; DAS; Machine Learning; Depression; Anxiety; Stress; Gaussian Naive Bayes; Neural Networks
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
R Medicine > RA Public aspects of medicine > RA790 Mental Health
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites > Online social networks
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites > Online social networks
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 14 Mar 2023 15:59
Last Modified: 14 Mar 2023 15:59

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