A class-imbalanced hybrid learning strategy based on Raman spectroscopy of serum samples for the diagnosis of hepatitis B, hepatitis A, and thyroid dysfunction.

Computer-aided vibrational spectroscopy detection technology has achieved promising results in the field of early disease diagnosis. Yet limited by factors such as the number of actual samples and the cost of spectral acquisition in clinical medicine, the data available for model training are insufficient, and the amount of data varies greatly between different diseases, which constrain the performance optimization and enhancement of the diagnostic model. In this study, vibrational spectroscopy data of three common diseases are selected as research objects, and experimental research is conducted around the class imbalance situation that exists in medical data. When dealing with the challenge of class imbalance in medical vibrational spectroscopy research, it no longer relies on some kind of independent and single method, but considers the combined effect of multiple strategies. SVM, K-Nearest Neighbor (KNN), and Decision Tree (DT) are used as baseline comparison models on Raman spectroscopy medical datasets with different imbalance rates. The performance of the three strategies, Ensemble Learning, Feature Extraction, and Resampling, is verified on the class imbalance dataset by G-mean and AUC metrics, respectively. The results show that all the above three methods mitigate the negative impact caused by unbalanced learning. Based on this, we propose a hybrid ensemble classifier (HEC) that integrates resampling, feature extraction, and ensemble learning to verify the effectiveness of the hybrid learning strategy in solving the class imbalance problem. The G-mean and AUC values of the HEC method are 82.7 % and 83.12 % for the HBV dataset, is 2.02 % and 1.98 % higher than the optimal strategy; 83.62 % and 83.76 % for the HCV dataset, is 9.79 % and 8.47 % higher than the optimal strategy; while for the thyroid dysfunction dataset are 77.56 % and 77.85 %, is 6.92 % and 6.36 % higher than that of the optimal strategy, respectively. The experimental results show that the G-mean and AUC metrics of the HEC method are higher than those of the baseline classifier as well as the optimal combination using separate strategies. It can be seen that the HEC method can effectively counteract the unfavorable effects of imbalance learning and is expected to be applied to a wider range of imbalance scenarios.

Bearing fault detection by using graph autoencoder and ensemble learning.

The research and application of bearing fault diagnosis techniques are crucial for enhancing equipment reliability, extending bearing lifespan, and reducing maintenance expenses. Nevertheless, most existing methods encounter challenges in discriminating between signals from machines operating under normal and faulty conditions, leading to unstable detection results. To tackle this issue, the present study proposes a novel approach for bearing fault detection based on graph neural networks and ensemble learning. Our key contribution is a novel stochasticity-based compositional method that transforms Euclidean-structured data into a graph format for processing by graph neural networks, with feature fusion and a newly proposed ensemble learning strategy for outlier detection specifically designed for bearing fault diagnosis. This approach marks a significant advancement in accurately identifying bearing faults, highlighting our study's pivotal role in enhancing diagnostic methodologies.

Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method.

According to the limited molecular information reflected by single spectroscopy, and the complementarity of FTIR spectroscopy and Raman spectroscopy, we propose a novel diagnostic technology combining multispectral fusion and deep learning. We used serum samples from 45 healthy controls, 44 non-small cell lung cancer (NSCLC), 38 glioma and 37 esophageal cancer patients, and the Raman spectra and FTIR spectra were collected respectively. Then we performed low-level fusion and feature fusion on the spectral, and used SVM, Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM) and the multi-scale convolutional fusion neural network (MFCNN). The accuracy of low-level fusion and feature fusion models are improved by about 10% compared with single spectral models.

Grouped-sampling technique to deal with unbalance in Raman spectral data modeling.

Due to limitations in disease prevalence and hospital specificity, spectral data are often collected with unbalanced sample size. To solve this problem, a new sampling method - grouped-sampling was proposed in this research, which is shown to be effective for unbalanced data. It avoids over-fitting of over-sampling and overcomes under-sampling utilization of under-sampling. In this study, we applied grouped-sampling to two unbalanced datasets where the sample proportions are 199:40 and 75:225. And then verified from two classic models: PCA-SVM (Principal Component Analysis-Support Vector Machine) and the deep learning algorithm GoogLeNet. The accuracy of these two datasets were 85.11% and 96.15% in PCA-SVM and 85.10% and 84.61% on GoogLeNet. Also, the F1-score were evaluated to measure the classification balance of sampling method, and result shows that F1-score of grouped-sampling is always the highest compared to over-sampling and under-sampling. In summary, compared to traditional sampling methods, grouped-sampling performs better on prediction for classes with smaller sample size, which means grouped-sampling can improve the balance of classification results and the potential of practical application. Therefore, we develop a group sampling method that distinguishes between under- and over-sampling, which greatly improves the accuracy and balance of predictions for unbalanced samples.