Review on Early Prediction of Body Dysmorphic Disorder Using Machine Learning

Body Dysmorphic Disorder (BDD) is a psychiatric condition characterized by excessive preoccupation with perceived defects in one's appearance, often leading to distress and functional impairment. Early prediction of BDD is crucial for timely intervention and improved treatment outcomes. In recent years, machine learning techniques have shown promise in identifying individuals at risk of developing BDD based on various data sources, including facial images, clinical information, and textual data. This review aims to provide an overview of the current state of research on the early prediction of BDD using machine learning, highlighting the methodologies, challenges, and future directions in this emerging field. We conducted a comprehensive literature search and identified relevant studies that employed machine learning algorithms for BDD prediction. The reviewed studies demonstrated the effectiveness of machine learning techniques such as support vector machines, deep learning models, ensemble learning, natural language processing, feature selection and extraction, and clustering in predicting BDD. Despite the promising results, several challenges need to be addressed, including the availability of large and diverse datasets, standardization of evaluation metrics, and interpretability of the models. Future research should focus on refining the existing approaches, integrating multiple data modalities, and developing user-friendly tools for clinical practice. Early prediction of BDD using machine learning holds great potential for improving the identification and intervention of individuals at risk, ultimately leading to better outcomes and quality of life for individuals with BDD.