Data-centric AI to Improve Early Detection of Mental Illness

The growth of information technology and advancements in artificial intelligence (AI) have made data creation and usage more prevalent. AI research can be grouped into two categories: model-centric and data-centric. Model-centric AI focuses on using the same data and making changes to model hyper-parameters, architectures, and other configurations. Data-centric AI, on the other hand, prioritizes improving existing data or incorporating new data to improve the performance of machine learning (ML) algorithms. Data-centric AI can greatly improve the performance of machine learning models by improving data quality, increasing data diversity, and using advanced data augmentation methods. The use of ML for early detection of mental health issues is vital due to its ability to identify issues early, provide personalized treatments, detect patterns, and increase accessibility to mental health services. While there have been numerous mental illness detection studies using model-centric approaches, there is a lack of research from a data-centric AI perspective. This study aims to address this gap by comparing established tabular data synthesis methods to explore the impact of synthetic data and data-centric AI on the early detection of mental health issues.

I. Introduction

The growth of information technology and advancements in artificial intelligence (AI) have dramatically increased data creation and usage. In AI systems, algorithms and data are two crucial elements. As a result, AI research can be divided into two categories: model-centric AI and data-centric AI [1]. Model-centric AI focuses on improving the performance of individual models, while data-centric AI focuses on enhancing the quality of data for machine learning (ML) tasks [2]. As a traditional approach, model-centric AI focuses on using the same data and making changes to model hyper-parameters, architectures, and other configurations. The data-centric approach, on the other hand, prioritizes improving existing data or incorporating new data, then training and evaluating ML algorithms. Overall, data-centric AI can generate new synthetic data, augment existing data, and balance class distributions, helping to overcome the limitations of limited data and improve model performance [3].