Mental disorder classification with exploratory data analysis (EDA)
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Abstract
Classification of mental disorders is the process of grouping mental disorders into categories based on their symptoms, causes and consequences. EDA is a data analysis strategy that emphasizes open-mindedness, creativity and diverse perspectives. EDA aims to explore as much data as possible, without imposing previous assumptions or models, until a coherent, coherent story emerges. EDA can help generate new hypotheses, identify patterns and outliers, and uncover underlying structures and relationships in data. This paper shows how EDA can be used to analyze and understand mental disorders data from a variety of sources and perspectives. We used EDA methods to explore the characteristics, prevalence, and distribution of mental disorders, as well as the relationships and interactions between mental disorders and other variables. We also compared EDA results with mental disorder classification systems such as the Diagnostic and Statistical Manual of Mental Disorders (DSM). We show that EDA can provide a more comprehensive and nuanced understanding of mental disorder data, as well as highlight the challenges and limitations of mental disorder classification. We hope this paper will illustrate the potential and benefits of EDA for mental disorders research and practice
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