# Sample-Pair Envelope Diamond Autoencoder Ensemble Algorithm for Chronic Disease Recognition
## 5 Discussion and Conclusions  
In recent years, chronic diseases have become a serious threat to human health. Currently, a practical approach is to first use wearable sensors to collect data from the human body and then process the data using machine learning. Machine learning algorithm is an effective tool for the analysis of sensing data of chronic disease. The machine learning method mainly includes two parts: feature learning and classifier, where feature learning is very important. Therefore, it is important and challenging to study highly efficient feature learning methods for sensor monitoring of chronic disease. Traditional feature learning methods are restricted from original features and cannot construct high-quality features, whereas deep feature learning methods suffer from the small-sample problems. To overcome these limitations, this paper proposed a solution--sample-pair envelope diamond stacked sparse autoencoder ensemble learning algorithm (SP_DFseaLA). First, the sample-pair envelope manifold neighborhood concatenation mechanism (SP_EMNCM) is designed by searching the manifold nearest samples in manifold neighborhood and generating sample pairs. Second, the feature embedding stacked sparse autoencoder (FESSAE) is designed to extend features. Third, a staged feature reduction mechanism is designed to reduce extended feature redundancy. The first stage uses the L1 regularized feature-reduction algorithm and the second stage uses an improved manifold dimensionality reduction algorithm to further reduce features. Fourth, the sample-pair-based model and single-sample-based model are combined by weighted fusion. In the experimental section, there sets of experiments are organized to verify the proposed method's effectiveness to validate the effectiveness of the innovation points and to research the algorithm's parameters. The results show that the SP_EMNCM is valid apparently. Compared with feature extraction methods, such as LDA, the accuracy of SP_DFseaLA's classification recognition is higher because the chronic disease samples are often random. However, LDA is not sensitive to non-Gaussian-distributed samples. Compared with feature selection algorithms, including Relief, the advantages of SP_DFseaLA are its greater learnability and lower tolerance to faults. Relief gives higher weights to all features that are correlated with the class, so the limitation of Relief is in that it does not effectively remove redundant features. Compared with deep learning methods (SAEs), the accuracy of SP_DFseaLA is improved by up to 20.5%. However, SAE only considers the feature extraction without considering the intrinsic structural information between samples. As mentioned previously, effective feature learning of sensor data for chronic diseases is important and challenging, and there are some contributions for solving the problem. Firstly, a diamond-like feature learning mechanism is proposed in this paper. This mechanism combines deep learning and traditional feature-reduction methods' advantages  
Figure 11: Effect of the number of classifiers on accuracy. and can better adapt to the data characteristics of chronic diseases and the requirement for high recognition accuracy than the deep learning and traditional feature-reduction methods. Secondly, this paper proposes a sample-pair envelope manifold neighborhood concatenation mechanism (SP_EMNCM), which has the advantage of enriching the feature information. Thirdly, this paper designs lightweight FESSAE, which has the advantage of improving the complementarity of original features with deep features and achieving high-quality deep features in small or medium sample sizes. Fourth, a feature expansion mechanism is proposed by combining SP_EMNCM and FESSAE, which has the advantage of improving the original features richer and better by considering the sample structure relationship and feature complementary relationship.