A Comprehensive Framework Leveraging Predictive Models for Robust Missing Data Handling

Missing data (MD) is an inherent challenge that arises for various reasons across different domains. It often results in incomplete datasets, which can compromise the quality of information processing and, in turn, reduce the reliability and relevance of the resulting decisions. Although the literature offers a wide range of methods and techniques to address MD challenges, many approaches still lack accuracy and fail to capture the underlying correlations and patterns within datasets. Furthermore, existing solutions have yet to fully exploit recent advances in artificial intelligence (AI) to enhance the effectiveness of MD handling. In this paper, we present a comprehensive framework for effectively addressing MD. In the proposed approach, missing values in structured datasets are imputed using the Random Forest (RF) algorithm, while sequential and time-series data are handled through the Long Short-Term Memory (LSTM) recurrent neural network (RNN), which captures both temporal dependencies and long-range patterns. The framework also allows users to define customized strategies for enhanced MD handling, where a user-defined strategy is formulated as a composition of existing imputation techniques combined with the proposed predictive models. To support practical adoption, the system has been implemented in a dedicated software tool that offers an efficient and flexible solution for diverse dataset formats with varying levels of missingness. Experiments on the London Weather (LWD) and PhysioNet Intensive Care Unit (ICU) benchmark datasets demonstrate the effectiveness of the proposed method, revealing clear improvements over traditional methods.