Internet of Things (IoT); clinical IoT; Haar transform; artificial intelligence; machine learning (ML)

This paper presents a study of feature engineering for the application of machine learning (ML) in clinical data processing, focusing on binary classification of time series data. The study demonstrates the effectiveness of using the Haar transform to enhance feature importance and improve classification performance. The Haar transform allows for increased predictive accuracy by augmenting the weight of significant features, which is especially crucial in handling complex clinical data.