Traditional Diagnosis Research Articles

VoiceAD: A multilingual speech based classifier for early detection of Alzheimers disease

Alzheimer's Disease (AD) causes memory decline beyond normal aging and is the most common cause of dementia. Early detection and intervention can delay the progression of AD and alleviate symptoms. However, high expense, insensitivity, and inconvenience are among various deterrents to traditional diagnosis methods. Dementia impacts more than 55 million people globally, with this number set to double every 20 years. Much of this increase will be in low- and middle-income countries which often lack the resources for early diagnosis. Recently speech has emerged as an effective digital biomarker for AD detection. However, most studies in this area are limited to English speech due to lack of suitable data in other languages. This study aims to construct a speech-based classifier (VoiceAD) that can be trained on English data and still maintain its performance on other languages. Acoustic features were extracted from English, Spanish, and Greek data from DementiaBank with the eGeMAPS v2.0 feature set. Subsequently, an Extra Trees model was employed for classification. VoiceADs performance for English and Greek surpassed that of the baseline model and model incorporating only demographic data while the Spanish performance was lower due to task effect. In addition, VoiceADs satisfactory generalizability and interpretability positions it as a promising candidate for clinical applications. It can be implemented as a free screening tool such as a mobile application to monitor AD risk. VoiceADs crosslingual applicability can its enable worldwide deployment, especially in settings where traditional methods of diagnosis are less attainable.

Dental cavity analysis, prediction, localization, and quantification using computer vision

Dental health assessment is a critical component of overall well-being, and advancements in computer vision and deep learning have opened new avenues for automating and enhancing this process. In this study, we present a comprehensive approach to dental cavity analysis, spanning localization, quantification, and visualization. Our methodology leveraged a diverse dataset of colored dental images that had been meticulously augmented and annotated. The You Only Look Once model was employed for precise dental cavity localization, providing bounding box predictions. Remarkably, these results were obtained based on images from standard device cameras. Subsequently, we introduced the use of the segment anything model segmentation model, known for its zero-shot generalization capabilities, to focus on the exact areas of dental cavities. This approach enhanced the granularity of our analysis, providing dental professionals with detailed visualizations for precise diagnosis. During the quantification phase, we extracted cavity areas from bounding box coordinates, enabling accurate measurement of cavity sizes. The model achieved a notable mean average precision of 0.732, an accuracy of 0.789, and a recall of 0.701. Moreover, the model converged quickly, with most metrics achieving near-optimal results after 100 iterations. This quantitative data augments traditional diagnosis methods, facilitating more informed treatment decisions.

Advancement in transformer fault diagnosis technology

The transformer plays a critical role in maintaining the stability and smooth operation of the entire power system, particularly in power transmission and distribution. The paper begins by providing an overview of traditional fault diagnosis methods for transformers, including dissolved gas analysis and vibration analysis techniques, elucidating their developmental trajectory. Building upon these traditional methods, numerous researchers have aimed to enhance and optimize them through intelligent technologies such as neural networks, machine learning, and support vector machines. These researchers have addressed common issues in traditional fault diagnosis methods, such as the low correlation between characteristic parameters and faults, ambiguous fault descriptions, and the complexity of feature analysis. However, due to the complexity of transformer structures and the uncertainties in operating environments, the collection and analysis of characteristic parameters becomes highly intricate. Researchers have further refined algorithms and feature values based on intelligent diagnostic algorithms for transformers. The goal is to improve diagnostic speed, mitigate the impact of measurement noise, and further advance the adaptability of artificial intelligence technology in the field of transformers. On the other hand, the excellent multi-parameter analysis capability of artificial intelligence technology is more suitable for transformer diagnostic techniques that involve the fusion of multiple information sources. Through the powerful data acquisition, processing, and decision-making capabilities provided by intelligent algorithms, it can comprehensively analyze non-electrical parameters such as oil and gas characteristics, vibration signals, temperature, along with electrical parameters like short-circuit reactance and load ratio. Moreover, it can automatically analyze the inherent relationship between faults and characteristic quantities and provide decision-making suggestions. This technique plays a pivotal role in ensuring transformer safety and power network security, emerging as a prominent direction in transformer fault diagnosis research.

Development and validation of a deep learning-based model for diabetes diagnosis

Diabetes is a systemic disease caused by hyperglycemia, and the number of people with diabetes worldwide may reach 1.31 billion by 2050. The traditional diagnosis of early diabetes is difficult and inaccurate. Computer-aided automatic method has been widely applied in diabetes diagnosis at early stage. In order to realize automatic diagnosis of diabetes, this study proposed a deep neural network-based model for diabetes diagnosis, and its performance was compared with those of other classical machine leaning models. After data cleaning, Synthetic Minority Over-sampling Technique (SMOTE) sampling and feature selecting of the diabetes dataset, multiple models were applied to the features such as Body Mass Index (BMI), Age and other features for prediction. The results showed that Deep Neural Network (DNN) is the best diagnostic solution for diabetes with excellent performance with an accuracy of 99.5%. More specific features will be considered to ensure the accuracy and credibility of clinical diagnosis of diabetes.

Lightweight bearing fault diagnosis method based on cross-scale learning transformer under imbalanced data*

Abstract Due to the limited amount of failure data in rolling bearing faults, traditional fault diagnosis models encounter challenges such as low diagnostic accuracy and efficiency when dealing with imbalanced data. Additionally, many fault diagnosis models are overly complex and demand high computational resources. To address these issues, a lightweight bearing fault diagnosis method based on cross-scale learnable transformer (CSLT) is proposed for imbalanced data. For difficult-to-classify samples, a learnable generalized focal loss function is defined. The learnable parameters are employed to increase its flexibility, it better addresses the issue of bearing fault diagnosis under imbalanced data conditions. Then, a multi-head broadcasted self-attention mechanism is designed by capturing critical local features of the signal through one-dimensional convolution operations, which not only improves feature extraction capability but also reduces computational complexity. Finally, a dynamic label prediction pruning module is developed to trim redundant labels, which helps in lightening the model and enhancing both feature extraction and diagnostic efficiency. The experimental results demonstrate that the proposed diagnosis method exhibits superior diagnostic precision and efficiency by comparing with other methods.

Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks.

Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can be markedly improved through the prompt identification of the causative pathogens and their susceptibility to antibiotics and antimicrobial agents. Traditional BSI diagnosis via blood culture is often hindered by its lengthy incubation period and its limitations in detecting pathogenic bacteria and their resistance profiles. Surface-enhanced Raman scattering (SERS) has recently gained prominence as a rapid and effective technique for identifying pathogenic bacteria and assessing drug resistance. This method offers molecular fingerprinting with benefits such as rapidity, sensitivity, and non-destructiveness. The objective of this study was to integrate deep learning (DL) with SERS for the rapid identification of common pathogens and their resistance to drugs in BSIs. To assess the feasibility of combining DL with SERS for direct detection, erythrocyte lysis and differential centrifugation were employed to isolate bacteria from blood samples with positive blood cultures. A total of 12,046 and 11,968 SERS spectra were collected from the two methods using Raman spectroscopy and subsequently analyzed using DL algorithms. The findings reveal that convolutional neural networks (CNNs) exhibit considerable potential in identifying prevalent pathogens and their drug-resistant strains. The differential centrifugation technique outperformed erythrocyte lysis in bacterial isolation from blood, achieving a detection accuracy of 98.68% for pathogenic bacteria and an impressive 99.85% accuracy in identifying carbapenem-resistant Klebsiella pneumoniae. In summary, this research successfully developed an innovative approach by combining DL with SERS for the swift identification of pathogenic bacteria and their drug resistance in BSIs. This novel method holds the promise of significantly improving patient prognoses and optimizing healthcare efficiency. Its potential impact could be profound, potentially transforming the diagnostic and therapeutic landscape of BSIs.

Machine Learning Driven Feature Extraction and Dimensionality Reduction for Image Classification

Cancer is the leading cause of death globally, affecting various organs in the human body. Early diagnosis of gastric cancer is essential for improving survival rates. However, traditional diagnosis methods are time-consuming, require multiple tests, and rely on specialist availability. This motivates the development of automated techniques for diagnosing gastric cancer using image analysis. While existing computerized techniques have been proposed, challenges remain. These include difficulty distinguishing healthy from cancerous regions in images and extracting irrelevant features during analysis. This research addresses these challenges by proposing a novel deep learning-based method for gastric cancer classification. The method utilizes deep feature extraction, dimensionality reduction, and classification techniques applied to a gastric cancer image dataset. This approach achieves high accuracy (99.32%), sensitivity (99.13%), and specificity (99.64%) in classifying gastric cancer.

Wearable solid-phase microextraction sampling for enhanced detection of volatile analytes in human ears

BackgroundInvestigating ear at molecule level is challenging task, since there is a lack of molecular detection by traditional diagnosis techniques such as otologic endoscopy, ear swab culture, and imaging diagnostic technique. Therefore, new development of noninvasive, highly sensitive, and convenient analytical method for investigating human ears is highly needed. ResultsWe developed a wearable sampling device for extracting trace analytes in ear by fixing solid-phase microextraction fibers into modified earmuffs (SPME-in-earmuffs). After sampling, SPME fiber was coupled with gas chromatography-mass spectrometry (GC-MS) for identification and quantification of extracted analytes. Enhanced detection of various analytes such as volatile metabolites, exposures, and therapeutic drugs of ears were demonstrated in this work. Particularly, sport-induced metabolic changes such as fatty acids, aldehyde compounds and oxidative produces were found from human ears using this method. Acceptable analytical performances were obtained by using this newly developed method for detecting ear medicines, e.g., low limit of detection (LOD, 0.005–0.021 ng/mL) and limit of quantification (LOQ, 0.018–0.071 ng/mL), excellent linear dynamic responses (R2 > 0.99, ranging from 0.050–8.00 ng/mL), good relative standard deviations (RSDs, 13.19 % ∼ 21.40 %, n = 6) and accuracy (84.43–150.18 %, n = 6) at different concentrations. SignificanceFor the first time, this work provides a simple, convenient, and wearable microextraction method for enhanced detection of trace volatiles in human ears. The enclosed space between ear and earmuff allows headspace SPME sampling of volatile analytes, and thus provides a new wearable method for monitoring ear metabolites and human exposures, showing potential applications in human health, disease diagnosis, and sport science.

Research on Failure Characteristics of Electric Logistics Vehicle Powertrain Gearbox Based on Current Signal

As a core component of the powertrain system of Electric Logistics Vehicles (ELVs), the gearbox is crucial for ensuring the reliability and stability of ELV operations. Traditional fault diagnosis methods for gearboxes primarily rely on the analysis of vibration signals during operation. This paper presents research on diagnosing gear tooth wear faults in ELV powertrains using motor current signals. Firstly, an experimental test platform was constructed based on the structural principle of the powertrain of ELV models. Subsequently, a pure electric light truck powertrain gearbox with tooth wear was tested. Time–frequency domain analysis, amplitude analysis, ANOVA analysis, kurtosis analysis, and zero−crossing points analysis were used to analyze the U−phase current of the motor connected to the gearbox to study the characteristics of the phase current of the drive motor after tooth wear. The results indicate that while the time–frequency domain characteristics of the U−phase currents are not significantly altered by tooth wear faults, the amplitude, variance, and kurtosis of the current increase with the severity of the wear. Conversely, the number of zero−crossing points decreases. These findings provide valuable insights into new methodologies for diagnosing faults in ELV powertrain systems, potentially enhancing the efficiency and effectiveness of troubleshooting processes.

Models for depression recognition and efficacy assessment based on clinical and sequencing data

Major depression is a complex psychiatric disorder that includes genetic, neurological, and cognitive factors. Early detection and intervention can prevent progression, and help select the best treatment. Traditional clinical diagnosis tends to be subjective and misdiagnosed. Based on this, this study leverages clinical scale assessments and sequencing data to construct disease prediction models. Firstly, data undergoes preprocessing involving normalization and other requisite procedures. Feature engineering is then applied to curate subsets of features, culminating in the construction of a model through the implementation of machine learning and deep learning algorithms. In this study, 18 features with significant differences between patients and healthy controls were selected. The depression recognition model was constructed by deep learning with an accuracy of 87.26 % and an AUC of 91.56 %, which can effectively distinguish patients with depression from healthy controls. In addition, 33 features selected by recursive feature elimination method were used to construct a prognostic effect model of patients after 2 weeks of treatment, with an accuracy of 75.94 % and an AUC of 83.33 %. The results show that the deep learning algorithm based on clinical and sequencing data has good accuracy and provides an objective and accurate method for the diagnosis and pharmacodynamic prediction of depression. Furthermore, the selected differential features can serve as candidate biomarkers to provide valuable clues for diagnosis and efficacy prediction.

Role of traditional Chinese medicine-based acupuncture in the resolution of ovarian torsion

Abstract Ovarian torsion (OT) is a gynecological emergency that affects females of childbearing age and usually requires surgical management. Here, we present a 30-year-old female who presented with an ultrasonographically confirmed OT along with an ovarian cyst and severe pain in the lower abdomen. She was treated at a private acupuncture clinic as an outpatient. She underwent one acupuncture session per week for 11 weeks. St 40, Sp6, Sp9, Lv3, Li4, CV4, St 29, K6, Lu7, Sp4, and Pc6 were selected and punctured based on traditional Chinese medicine diagnosis. The patient reported mild relief from pain immediately after the first session. A complete relief of pain was reported by the patient by the end of the third session. A follow-up ultrasound revealed no significant ovarian abnormality. This report indicates the possibility of using acupuncture in emergency conditions like OT.

Intelligent diagnosis method for machine faults based on federated transfer learning

Intelligent fault diagnosis model based on federated learning can effectively solve the problem of fault data privacy and sharing, and ignores the difference of fault data distribution. Transfer learning can avoid the difference of data distribution. Combining the advantages of transfer learning and federated learning, a fault diagnosis model with data privacy based on federated transfer learning is proposed to achieve cross-domain fault diagnosis without sharing the data. In the constructed model, the local models on the client are firstly established based on deep convolution neural network to extract the feature of the source and target domains. The alignment loss is introduced to minimize the similar feature distribution differences among different source domains and target domain. The parameters of local models are fused and updated to generate a global model, which can not only identify the fault types in the target domain, but also retain the ability to recognize the fault types in the source domain. The two experiments, including different bearings with same feature distribution and label and the bearing and planetary gear in the same transmission system with similar feature distribution, are used to verify the effectiveness of the proposed model. The experiments suggest that the fault diagnosis model based on federated transfer learning can reduce the difference of the newly added fault type data distribution, and can accurately recognize the fault data of the source domain and target domain. Compared with the traditional diagnosis model based on deep learning, transfer learning and federated learning, the proposed model can effective perform the cross-domain fault diagnosis with data privacy.

Rotating machinery weak fault features enhancement via line-defect phononic crystal sensing

The fault features of rotating machinery at the early degradation stage are always weak as a result of interference from strong noise and irrelevant harmonics. Although traditional acoustic diagnosis techniques have attracted much attention with the merits of rich information and non-contact, extracting meaningful features related to faults in extremely low signal-to-noise ratios (SNRs) has always been a challenging problem. Considering the extraordinary physical properties of phononic crystals (PnCs) and acoustic metamaterials, this study proposes a structural enhancement method for rotating machinery fault diagnosis via line-defect PnC sensing. In contrast to conventional acoustic filtering and enhancement methods, this method can directly filter out noise and enhance fault features in the pre-processing stage of acoustic perception without the need for complex post-processing algorithms. Consequently, the original information of the fault features is preserved intact, thus further increasing the detection limit of current acoustic sensing. Specially, the designed line-defect PnC is parametrically tunable. Combined with the prior knowledge of rotating machinery fault features, such as gears and bearings, it is possible to design structures suitable for enhancing their fault features, which has great potential for practical engineering applications. The enhancement mechanism of line-defect PnC is theoretically described, and numerical simulations are also conducted to verify its ability to detect weak faults in rotating machinery. The experimental results show that by comparison with the variational modal decomposition (VMD)-based method, the proposed method exhibits superior fault feature enhancement performance under low SNR conditions. By systematically combining fault detection methods and acoustic metamaterial sensing, it can be foreseen that the proposed method shows great potential in mechanical equipment fault diagnosis.

Culturally-informed neuropsychological evaluations of patients with spina bifida: Application of the ECLECTIC framework to a complex pediatric medical condition

Objective: The pediatric patient populations in the United States are becoming increasingly diverse in culture. In addition to medical factors, a variety of sociocultural factors (including educational systems, language, immigration status, etc.) can impact a child’s cognitive development, performance on traditional neuropsychological measures, provider interactions, differential diagnosis, and recommendations. A culturally-informed neuropsychological evaluation aims to understand the impact of sociocultural factors and integrate them into the assessment approach. Fujii’s ECLECTIC framework (acronym for Education & Literacy, Culture/Acculturation, Language, Economic Issues, Communication Style, Testing Situation, Intelligence Conceptualization, and Context of Immigration) can be used to guide culturally-informed neuropsychological evaluations of children with complex medical conditions. Method: Using the ECLECTIC framework, this paper describes clinical considerations and offers practical solutions for providing culturally-informed pediatric neuropsychology evaluations. Four diverse patients with spina bifida are reviewed. Results: The case presentations highlight the application of the ECLECTIC framework within a medically complex pediatric patient population. Conclusions: Spina bifida is one example of a pediatric medical population in which both medical and sociocultural factors can impact cognitive development and the evaluation process. The ECLECTIC framework defines a variety of sociocultural factors that can influence cognitive development and multiple aspects of a pediatric neuropsychological evaluation. Pediatric neuropsychologists should use the ECLECTIC framework to provide culturally-informed evaluations for our increasingly diverse patient populations.

A quantitative internal-short-circuit diagnosis method of lithium-ion batteries for float charging systems

The diagnosis of internal short circuit (ISC) faults in lithium-ion batteries (LIBs) plays an important role in improving battery safety and reducing the occurrence of fire and explosion accidents. Traditional ISC diagnosis methods mainly focus on dynamic operating conditions, and rarely consider stable float charging scenarios with high risks. This paper proposes a quantitative diagnosis strategy for ISC faults in LIBs under different float charging conditions. First, a simulation model of ISC faults for single cells and battery modules under float charging conditions is established. Then, for the LIB packs without a balancing system, three quantitative diagnosis methods (namely Map-based, constant-voltage-source based, and intermittent-charging based) for the ISC faults applicable to different simple and complex float charging systems are proposed. Finally, the method is verified through simulation and experiments, and the results show that an ISC of 0–500 Ω can be detected in various scenarios, and the proposed methods are low-cost, high-precision, and easy to implement for early ISC diagnosis of float charging systems for energy storage.

Machine Learning and Natural Language Processing Algorithms in the Remote Mobile Medical Diagnosis System of Internet Hospitals

In order to alleviate the contradiction between supply and demand of professional pharmacists, integrate medical resources, and ensure the safety of patients’ medication, the telemedicine diagnosis system has played a great role. Today, in this “Internet +” era, all walks of life have begun to integrate with Internet technology. The purpose of this article was to discuss the practical utility of machine learning and natural language processing algorithms in the remote mobile medical diagnosis system of Internet hospitals, for which this article conducted in-depth discussion. This article first introduced the basic concepts, development, and characteristics of machine learning and natural language processing algorithms in detail, and carefully studied and analyzed the development and culture of traditional offline medical diagnosis models. Based on machine learning and natural language processing algorithms, a remote mobile medical diagnosis is designed. By combining with the medical diagnosis system of traditional hospitals, a new type of remote mobile medical diagnosis system for Internet hospitals was designed and developed, and the combination of traditional medical industry and Internet technology was deeply studied. According to each functional requirement, the image module, heart rate measurement module, and user setting module are designed, respectively. Compared to traditional medical diagnosis systems, the accuracy of the remote mobile medical diagnosis system based on machine learning applied in Internet hospital diagnosis in this article reached 80% or even higher. At the same time, it was found through experiments that when the evolution number was 3, the maximum fit value and average fit value were the same, both of which were 0.6. This indicates that the system can accommodate more than 10,000 people at the same time, and patients can receive good treatment plans, with a very broad application prospect

A new indirect transfer fault diagnosis method based on feature separation

The existing fault diagnosis methods based on deep transfer learning achieve domain adaptation by matching shared features between the source and target domains. However, there are different private characteristics between the source domain and the target domain, and traditional cross-domain fault diagnosis algorithms ignore the impact of private attributes in the target domain on classification tasks. Meanwhile, the existence of private features causes a negative transfer problem for domain adaptation methods in domains with large aligned domain gaps. In practical applications, indirect transfer between the source domain and target domain can reduce the limitations of inherent attributes and enhance the guidance of private features in the target domain for classification tasks, which can enrich the transfer of fault knowledge and improve the effectiveness of cross-domain fault diagnosis. Therefore, an indirect transfer fault diagnosis method based on feature separation (ITFS) is proposed. Firstly, an attention-enhanced feature separation framework is proposed, which constructs a set of joint attention convolutional autoencoders (CAEs) to separate features from two domains, avoiding the influence of domain shift in private features. Secondly, to better adapt the two domains, an indirect domain adaptation method based on Gaussian distribution is proposed, which combines Gaussian indirect transfer learning with pseudo-labeling enhancement to reduce the domain gap in the two domains. Six transfer tasks on the PU datasets demonstrate that compared to MCD, the average accuracy of the proposed method is improved by 14.09 %, and it is superior to other popular existing methods. Experiments on three publicly available datasets have shown that this method outperforms other popular existing methods.

S5Utis: Structured State-Space Sequence SegNeXt UNet-like Tongue Image Segmentation in Traditional Chinese Medicine.

Intelligent Traditional Chinese Medicine can provide people with a convenient way to participate in daily health care. The ease of acceptance of Traditional Chinese Medicine is also a major advantage in promoting health management. In Traditional Chinese Medicine, tongue imaging is an important step in the examination process. The segmentation and processing of the tongue image directly affects the results of intelligent Traditional Chinese Medicine diagnosis. As intelligent Traditional Chinese Medicine continues to develop, remote diagnosis and patient participation will play important roles. Smartphone sensor cameras can provide irreplaceable data collection capabilities in enhancing interaction in smart Traditional Chinese Medicine. However, these factors lead to differences in the size and quality of the captured images due to factors such as differences in shooting equipment, professionalism of the photographer, and the subject's cooperation. Most current tongue image segmentation algorithms are based on data collected by professional tongue diagnosis instruments in standard environments, and are not able to demonstrate the tongue image segmentation effect in complex environments. Therefore, we propose a segmentation algorithm for tongue images collected in complex multi-device and multi-user environments. We use convolutional attention and extend state space models to the 2D environment in the encoder. Then, cross-layer connection fusion is used in the decoder part to fuse shallow texture and deep semantic features. Through segmentation experiments on tongue image datasets collected by patients and doctors in real-world settings, our algorithm significantly improves segmentation performance and accuracy.

Circulating tumor DNA (ctDNA) Detection via electrochemical Biosensing Tools

Cancer is characterized by the presence of mutated alleles in DNA, leading to the formation of tumors. A delayed diagnosis of this condition can result in fatal outcomes, making it a significant global cause of mortality. WHO has emphasized that early detection could significantly increase the chances of successful treatment and recovery. Traditional cancer diagnosis relies on invasive tissue biopsies, which pose risks to both patient’s and healthcare professionals due to the use of formaldehyde, a known carcinogenic agent, for specimen preservation. In recent times, liquid biopsies have emerged as a promising alternative, particularly for the analysis of circulating tumor DNA (ctDNA), a fraction of which originates from tumor cells and circulates in the bloodstream. However, conventional molecular genetic tests for ctDNA analysis are often costly and time-consuming. Advancements in technology and the field of nanoscience offer the potential to develop cost-effective, rapid, highly sensitive, and selective diagnostic tools. Among these, biosensors stand out as a promising option. In this article, we delve into the quantification of ctDNA in plasma, discuss amplification techniques for ctDNA, and explore the development of electrochemical-based biosensors tailored for ctDNA detection. Finally, we highlight recent studies and innovations in the field of ctDNA detection.

A GA based SVM-Bayesian technique and its application in process monitoring for mixed distributed data

Traditional data-driven fault diagnosis methods, such as principal component analysis (PCA) and independent component analysis (ICA), have significantly improved condition monitoring and fault diagnosis of industrial process by analyzing process data. However, their accuracy is limited due to the assumption that the data follows Gaussian or non-Gaussian distribution, respectively. In reality, process data is often a mixture of both Gaussian and non-Gaussian distributions, leading to reduced accuracy of the training model and fault diagnosis results. To address this issue, a genetic algorithm-based SVM-Bayesian (GSB) scheme is proposed in this paper for feature combination and model integration. The scheme first employs ICA and PCA to extract the independent/principal components of the original data, and then constructs the corresponding fault features for SVM training to obtain basic models. The extraction of fault features are optimized using genetic algorithms based on defined accuracy and diversity functions. During online monitoring, the final diagnosis result is obtained by combining the results of the basic models through Bayesian inference. The effectiveness of the proposed scheme is evaluated using a numerical example and a case study of the Tennessee Eastman process. The results show that the accuracy of fault classification is improved by about 5% compared with traditional methods.