Sample-Pair Envelope Diamond Autoencoder Ensemble Algorithm for Chronic Disease Recognition

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...

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Bibliographic Details
Published in:Applied sciences Vol. 13; no. 12; p. 7322
Main Authors: Zhang, Yi, Ma, Jie, Qin, Xiaolin, Li, Yongming, Zhang, Zuwei
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2023
Subjects:
Accuracy
Algorithms
Cardiovascular disease
Chronic diseases
Chronic illnesses
Decision trees
Deep learning
diamond-like feature learning
Discriminant analysis
envelope learning
Feature selection
feature-embedded stacked sparse autoencoder
Machine learning
Neural networks
Physiology
recognition of chronic disease
sample-pair envelope concatenation
Sensors
Type 2 diabetes
wearable sensor monitoring
China
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary: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.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app13127322