Mental Health Tracker For User’s Well-Being Using Machine Learning Techniques

Computer Engineering

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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