Utilizing Data Science Models to Evaluate Questionnaire Accuracy for Detecting Mental Illness: Insights and Applications for Psychological Assessments

Abstract

The accurate assessment of mental illness is crucial for effective diagnosis, treatment, and support. Traditional psychological assessments often rely on questionnaires to gather information about an individual's mental health. However, the accuracy of these questionnaires can vary, leading to potential misdiagnosis or underdiagnosis. This paper explores the application of data science models to evaluate the accuracy of questionnaires in detecting mental illness. The study proposes utilizing machine learning algorithms and statistical techniques to analyze questionnaire data and identify patterns that correlate with specific mental health conditions. By training these models on a diverse dataset of individuals with known mental health diagnoses, it is possible to create predictive models that can accurately identify mental illness based on questionnaire responses.