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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| Vydáno v: | Applied sciences Ročník 13; číslo 12; s. 7322 |
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| Jazyk: | angličtina |
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MDPI AG
01.06.2023
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| ISSN: | 2076-3417, 2076-3417 |
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| Abstract | 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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| AbstractList | 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. |
| Audience | Academic |
| Author | Zhang, Zuwei Li, Yongming Zhang, Yi Qin, Xiaolin Ma, Jie |
| Author_xml | – sequence: 1 givenname: Yi surname: Zhang fullname: Zhang, Yi – sequence: 2 givenname: Jie surname: Ma fullname: Ma, Jie – sequence: 3 givenname: Xiaolin surname: Qin fullname: Qin, Xiaolin – sequence: 4 givenname: Yongming orcidid: 0000-0002-7542-4356 surname: Li fullname: Li, Yongming – sequence: 5 givenname: Zuwei surname: Zhang fullname: Zhang, Zuwei |
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| SubjectTerms | 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 |
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| Title | Sample-Pair Envelope Diamond Autoencoder Ensemble Algorithm for Chronic Disease Recognition |
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