Resumen
Chronic diseases are severe and life-threatening, and their accurate early diagnosis is difficult. Machine-learning-based processes of data collected from the human body using wearable sensors are a valid method currently usable for diagnosis. However, it is difficult for wearable sensor systems to obtain high-quality and large amounts of data to meet the demands of diagnostic accuracy. Furthermore, existing feature-learning methods do not deal with this problem well. To address the above issues, a sample-pair envelope diamond autoencoder ensemble algorithm (SP_DFsaeLA) is proposed. The proposed algorithm has four main components. Firstly, sample-pair envelope manifold neighborhood concatenation mechanism (SP_EMNCM) is designed to find pairs of samples that are close to each other in a manifold neighborhood. Secondly, the feature-embedding stacked sparse autoencoder (FESSAE) is designed to extend features. Thirdly, a staged feature reduction mechanism is designed to reduce redundancy in the extended features. Fourthly, the sample-pair-based model and single-sample-based model are combined by weighted fusion. The proposed algorithm was experimentally validated on nine datasets and compared with the latest algorithm. The experimental results show that the algorithm is significantly better than existing representative algorithms and it achieves the highest improvement of 22.77%, 21.03%, 24.5%, 27.89%, and 10.65% on five criteria over the state-of-the-art methods.