Data Imputation with Deep Learning: AI Techniques for Handling Missing or Noisy Data

Abstract

Handling missing or noisy data is a critical challenge in data-driven applications across various domains, including healthcare, finance, and industrial systems. Traditional imputation techniques often rely on statistical methods that may fail to capture complex, nonlinear relationships within data. This paper explores the use of deep learning-based approaches for data imputation, offering robust and adaptive solutions to incomplete datasets. We review state-of-the-art models such as autoencoders, generative adversarial networks (GANs), and recurrent neural networks (RNNs), emphasizing their ability to learn latent patterns and reconstruct missing values with high accuracy. The paper also investigates hybrid models that integrate domain-specific knowledge with AI techniques to enhance imputation quality. Comparative analysis demonstrates the superiority of deep learning models over conventional methods in terms of precision, scalability, and adaptability to high-dimensional data. Additionally, we discuss challenges such as model interpretability, computational complexity, and the need for large, representative datasets. Applications across medical diagnosis, financial forecasting, and sensor-based monitoring systems are presented to highlight the practical benefits of deep learning in managing real-world data imperfections. Overall, this study provides a comprehensive overview of how deep learning advances data imputation, making AI a powerful ally in building reliable and complete datasets for critical decision-making.