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Title: :  Mental Health Tracker For User’s Well-Being Using Machine Learning Techniques
PaperId: :  19939
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 2 2023
DUI:    16.0415/IJARIIE-19939
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Nidhi R ShettySir M Visvesvaraya Institute of Technology, Bangalore
Shreya RajSir M Visvesvaraya Institute of Technology, Bangalore
Vartika SharmaSir M Visvesvaraya Institute of Technology, Bangalore
Susmita DebnathSir M Visvesvaraya Institute of Technology, Bangalore
K P MayuriSir M Visvesvaraya Institute of Technology, Bangalore

Abstract

Computer Engineering
Machine Learning, Mental Health, Accuracy, Machine Learning Algorithms, Supervised Learning, Depression, Mental Health Illness Prediction.
This paper highlights the significance of digital transformation particularly in the sphere of mental health care. Mental health influences how one feels and behaves. Bad mental health can lead to depression. Its early detection enables doctors to diagnose them more effectively. This study is to develop an application to track an individual’s mental health. Several accuracy criteria were used to assess the efficacy of four machine learning techniques in this study's diagnosis of mental health issues. KNN Classifier, Decision Tree Classifier, Logistic Regression, and SVM are the four Machine Learning algorithms. We compared these methods, put them into practice, and found the most accurate method. Based on these patterns, the tracker provides personalized recommendations for interventions that may help manage mental health conditions. Our evaluation results demonstrate the effectiveness of the mental health tracker in improving user well-being and suggest that it could be a useful tool for improving access to mental health care. Additionally, the mental health tracker has the potential to enhance the efficiency of mental health care by streamlining the process of identifying and addressing mental health concerns. It may also reduce the stigma associated with seeking help for mental health issues by providing a confidential and convenient way to track and manage mental health.

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IJARIIE Nidhi R Shetty, Shreya Raj, Vartika Sharma, Susmita Debnath, and K P Mayuri. "Mental Health Tracker For User’s Well-Being Using Machine Learning Techniques" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 2 2023 Page 2807-2812
MLA Nidhi R Shetty, Shreya Raj, Vartika Sharma, Susmita Debnath, and K P Mayuri. "Mental Health Tracker For User’s Well-Being Using Machine Learning Techniques." International Journal Of Advance Research And Innovative Ideas In Education 9.2(2023) : 2807-2812.
APA Nidhi R Shetty, Shreya Raj, Vartika Sharma, Susmita Debnath, & K P Mayuri. (2023). Mental Health Tracker For User’s Well-Being Using Machine Learning Techniques. International Journal Of Advance Research And Innovative Ideas In Education, 9(2), 2807-2812.
Chicago Nidhi R Shetty, Shreya Raj, Vartika Sharma, Susmita Debnath, and K P Mayuri. "Mental Health Tracker For User’s Well-Being Using Machine Learning Techniques." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 2 (2023) : 2807-2812.
Oxford Nidhi R Shetty, Shreya Raj, Vartika Sharma, Susmita Debnath, and K P Mayuri. 'Mental Health Tracker For User’s Well-Being Using Machine Learning Techniques', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 2, 2023, p. 2807-2812. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Mental_Health_Tracker_For_User’s_Well_Being_Using_Machine_Learning_Techniques_ijariie19939.pdf (Accessed : ).
Harvard Nidhi R Shetty, Shreya Raj, Vartika Sharma, Susmita Debnath, and K P Mayuri. (2023) 'Mental Health Tracker For User’s Well-Being Using Machine Learning Techniques', International Journal Of Advance Research And Innovative Ideas In Education, 9(2), pp. 2807-2812IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Mental_Health_Tracker_For_User’s_Well_Being_Using_Machine_Learning_Techniques_ijariie19939.pdf (Accessed : )
IEEE Nidhi R Shetty, Shreya Raj, Vartika Sharma, Susmita Debnath, and K P Mayuri, "Mental Health Tracker For User’s Well-Being Using Machine Learning Techniques," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 2, pp. 2807-2812, Mar-App 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/Mental_Health_Tracker_For_User’s_Well_Being_Using_Machine_Learning_Techniques_ijariie19939.pdf [Accessed : ].
Turabian Nidhi R Shetty, Shreya Raj, Vartika Sharma, Susmita Debnath, and K P Mayuri. "Mental Health Tracker For User’s Well-Being Using Machine Learning Techniques." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 2 ().
Vancouver Nidhi R Shetty, Shreya Raj, Vartika Sharma, Susmita Debnath, and K P Mayuri. Mental Health Tracker For User’s Well-Being Using Machine Learning Techniques. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : ]; 9(2) : 2807-2812. Available from: https://ijariie.com/AdminUploadPdf/Mental_Health_Tracker_For_User’s_Well_Being_Using_Machine_Learning_Techniques_ijariie19939.pdf
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