Feature Engineering for Mental Health Applications
Rithik Reddy Nadimpalli, Mahitha Jalapally, Alberto Acereda · Advances in computational intelligence and robotics book series · 2025
Feature engineering plays a pivotal role in developing accurate and effective mental health applications, as it enables the extraction of meaningful insights from raw data for precise mental health monitoring. This study explores the processes of data collection and preprocessing, focusing on transforming diverse data sources—including behavioral, physiological, and contextual data—into usable formats. Key strategies for identifying relevant features for mental health assessment are discussed, including the use of machine learning techniques to pinpoint indicators strongly correlated with specific mental health conditions. The primary research question addressed is: What strategies can be employed to enhance feature selection for precise mental health monitoring? By examining advanced methods such as automated feature selection, dimensionality reduction, and domain-specific feature engineering, this study aims to optimize the accuracy and reliability of mental health assessments.