الملخص
To realize the non-destructive and rapid origin discrimination of Poria cocos in batches, this study established the P. cocos origin recognition model based on hyperspectral imaging combined with machine learning. P. cocos samples from Anhui, Fujian, Guangxi, Hubei, Hunan, Henan and Yunnan were used as the research objects. Hyperspectral data were collected in the visible and near infrared band(V-band, 410-990 nm) and shortwave infrared band(S-band, 950-2 500 nm). The original spectral data were divided into S-band, V-band and full-band. With the original data(RD) of different bands, multiplicative scatter correction(MSC), standard normal variation(SNV), S-G smoothing(SGS), first derivative(FD), second derivative(SD) and other pretreatments were carried out. Then the data were classified according to three different types of producing areas: province, county and batch. The origin identification model was established by partial least squares discriminant analysis(PLS-DA) and linear support vector machine(LinearSVC). Finally, confusion matrix was employed to evaluate the optimal model, with F1 score as the evaluation standard. The results revealed that the origin identification model established by FD combined with LinearSVC had the highest prediction accuracy in full-band range classified by province, V-band range by county and full-band range by batch, which were 99.28%, 98.55% and 97.45%, respectively, and the overall F1 scores of these three models were 99.16%, 98.59% and 97.58%, respectively, indicating excellent performance of these models. Therefore, hyperspectral imaging combined with LinearSVC can realize the non-destructive, accurate and rapid identification of P. cocos from different producing areas in batches, which is conducive to the directional research and production of P. cocos.
الموضوعات
Hyperspectral Imaging , Wolfiporia , China , Least-Squares Analysis , Support Vector Machineالملخص
Heart valve disease (HVD) is one of the common cardiovascular diseases. Heart sound is an important physiological signal for diagnosing HVDs. This paper proposed a model based on combination of basic component features and envelope autocorrelation features to detect early HVDs. Initially, heart sound signals lasting 5 minutes were denoised by empirical mode decomposition (EMD) algorithm and segmented. Then the basic component features and envelope autocorrelation features of heart sound segments were extracted to construct heart sound feature set. Then the max-relevance and min-redundancy (MRMR) algorithm was utilized to select the optimal mixed feature subset. Finally, decision tree, support vector machine (SVM) and k-nearest neighbor (KNN) classifiers were trained to detect the early HVDs from the normal heart sounds and obtained the best accuracy of 99.9% in clinical database. Normal valve, abnormal semilunar valve and abnormal atrioventricular valve heart sounds were classified and the best accuracy was 99.8%. Moreover, normal valve, single-valve abnormal and multi-valve abnormal heart sounds were classified and the best accuracy was 98.2%. In public database, this method also obtained the good overall accuracy. The result demonstrated this proposed method had important value for the clinical diagnosis of early HVDs.
الموضوعات
Humans , Heart Sounds , Heart Valve Diseases/diagnosis , Algorithms , Support Vector Machine , Signal Processing, Computer-Assistedالملخص
This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
الموضوعات
Humans , Depression , Bayes Theorem , Machine Learning , Support Vector Machine , Blood Cell Countالملخص
This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
الموضوعات
Humans , Depression , Bayes Theorem , Machine Learning , Support Vector Machine , Blood Cell Countالملخص
When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.
الموضوعات
Animals , Support Vector Machine , Whales , Eye Movements , Algorithmsالملخص
Atrial fibrillation is a common arrhythmia, and its diagnosis is interfered by many factors. In order to achieve applicability in diagnosis and improve the level of automatic analysis of atrial fibrillation to the level of experts, the automatic detection of atrial fibrillation is very important. This study proposes an automatic detection algorithm for atrial fibrillation based on BP neural network (back propagation network) and support vector machine (SVM). The electrocardiogram (ECG) segments in the MIT-BIH atrial fibrillation database are divided into 10, 32, 64, and 128 heartbeats, respectively, and the Lorentz value, Shannon entropy, K-S test value and exponential moving average value are calculated. These four characteristic parameters are used as the input of SVM and BP neural network for classification and testing, and the label given by experts in the MIT-BIH atrial fibrillation database is used as the reference output. Among them, the use of atrial fibrillation in the MIT-BIH database, the first 18 cases of data are used as the training set, and the last 7 cases of data are used as the test set. The results show that the accuracy rate of 92% is obtained in the classification of 10 heartbeats, and the accuracy rate of 98% is obtained in the latter three categories. The sensitivity and specificity are both above 97.7%, which has certain applicability. Further validation and improvement in clinical ECG data will be done in next study.
الموضوعات
Humans , Atrial Fibrillation/diagnosis , Support Vector Machine , Heart Rate , Algorithms , Neural Networks, Computer , Electrocardiographyالملخص
OBJECTIVES@#To reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis (PCA) and partial least squares (PLS) methods. To establish a support vector machine (SVM) classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application.@*METHODS@#Eighty percent of 146 normal and injured pelvic CT images were randomly selected as training set for model fitting, and the remaining 20% was used as testing set to verify the accuracy of the test, respectively. Through CT image input, preprocessing, feature extraction, feature information dimension reduction, feature selection, parameter selection, model establishment and model comparison, a discriminative model of pelvic injury was established.@*RESULTS@#The PLS dimension reduction method was better than the PCA method and the SVM model was better than the naive Bayesian classifier (NBC) model. The accuracy of the modeling set, leave-one-out cross validation and testing set of the SVM classification model based on 12 PLS factors was 100%, 100% and 93.33%, respectively.@*CONCLUSIONS@#In the evaluation of pelvic injury, the pelvic injury data mining model based on CT images reaches high accuracy, which lays a foundation for automatic and rapid identification of pelvic injuries.
الموضوعات
Algorithms , Bayes Theorem , Data Mining , Least-Squares Analysis , Support Vector Machineالملخص
Background@#Cardiovascular diseases belong to the top three leading causes of mortality in the Philippines with 17.8 % of the total deaths. Lifestyle-related habits such as alcohol consumption, smoking, poor diet and nutrition, high sedentary behavior, overweight, and obesity have been increasingly implicated in the high rates of heart disease among Filipinos leading to a significant burden to the country's healthcare system. The objective of this study was to predict the presence of heart disease using various machine learning algorithms (support vector machine, naïve Bayes, random forest, logistic regression, decision tree, and adaptive boosting) evaluated on an anonymized publicly available cardiovascular disease dataset. @*Methodology@#Various machine learning algorithms were applied on an anonymized publicly available cardiovascular dataset from a machine learning data repository (IEEE Dataport). A web-based application system named Heart Alert was developed based on the best machine learning model that would predict the risk of developing heart disease. An assessment of the effects of different optimization techniques as to the imputation methods (mean, median, mode, and multiple imputation by chained equations) and as to the feature selection method (recursive feature elimination) on the classification performance of the machine learning algorithms was made. All simulation experiments were implemented via Python 3.8 and its machine learning libraries (Scikit-learn, Keras, Tensorflow, Pandas, Matplotlib, Seaborn, NumPy). @*Results@#The support vector machine without imputation and feature selection obtained the highest performance metrics (90.2% accuracy, 87.7% sensitivity, 93.6% specificity, 94.9% precision, 91.2% F1-score and an area under the receiver operating characteristic curve of 0.902 ) and was used to implement the heart disease prediction system (Heart Alert). Following very closely were random forest with mean or median imputation and logistic regression with mode imputation, all having no feature selection which also performed well. @*Conclusion@#The performance of the best four machine learning models suggests that for this dataset, imputation technique for missing values may or may not be done. Likewise, recursive feature elimination for feature selection may not apply as all variables seem to be important in heart disease prediction. An early accurate diagnosis leading to prompt intervention efforts is very crucial as it improves the patient's quality of life and diminishes the risk of developing cardiac events.
الموضوعات
Machine Learning , Support Vector Machineالملخص
In order to improve the motion fluency and coordination of lower extremity exoskeleton robots and wearers, a pace recognition method of exoskeleton wearer is proposed base on inertial sensors. Firstly, the triaxial acceleration and triaxial angular velocity signals at the thigh and calf were collected by inertial sensors. Then the signal segment of 0.5 seconds before the current time was extracted by the time window method. And the Fourier transform coefficients in the frequency domain signal were used as eigenvalues. Then the support vector machine (SVM) and hidden Markov model (HMM) were combined as a classification model, which was trained and tested for pace recognition. Finally, the pace change rule and the human-machine interaction force were combined in this model and the current pace was predicted by the model. The experimental results showed that the pace intention of the lower extremity exoskeleton wearer could be effectively identified by the method proposed in this article. And the recognition rate of the seven pace patterns could reach 92.14%. It provides a new way for the smooth control of the exoskeleton.
الموضوعات
Humans , Algorithms , Exoskeleton Device , Lower Extremity , Motion , Support Vector Machineالملخص
Heart sound signal is a kind of physiological signal with nonlinear and nonstationary features. In order to improve the accuracy and efficiency of the phonocardiogram (PCG) classification, a new method was proposed by means of support vector machine (SVM) in which the complete ensemble empirical modal decomposition with adaptive noise (CEEMDAN) permutation entropy was as the eigenvector of heart sound signal. Firstly, the PCG was decomposed by CEEMDAN into a number of intrinsic mode functions (IMFs) from high to low frequency. Secondly, the IMFs were sifted according to the correlation coefficient, energy factor and signal-to-noise ratio. Then the instantaneous frequency was extracted by Hilbert transform, and its permutation entropy was constituted into eigenvector. Finally, the accuracy of the method was verified by using a hundred PCG samples selected from the 2016 PhysioNet/CinC Challenge. The results showed that the accuracy rate of the proposed method could reach up to 87%. In comparison with the traditional EMD and EEMD permutation entropy methods, the accuracy rate was increased by 18%-24%, which demonstrates the efficiency of the proposed method.
الموضوعات
Entropy , Heart Sounds , Signal Processing, Computer-Assisted , Signal-To-Noise Ratio , Support Vector Machineالملخص
Abstract Improving the accuracy of protein secondary structure prediction has been an important task in bioinformatics since it is not only the starting point in obtaining tertiary structure in hierarchical modeling but also enhances sequence analysis and sequence-structure threading to help determine structure and function. Herein we present a model based on DSPRED classifier, a hybrid method composed of dynamic Bayesian networks and a support vector machine to predict 3-state secondary structure information of proteins. We used the SCOPe (Structural Classification of Proteins-extended) database to train and test the model. The results show that DSPRED reached a Q3 accuracy rate of 82.36% when trained and tested using proteins from all SCOPe classes. We compared our method with the popular PSIPRED on the SCOPe test datasets and found that our method outperformed PSIPRED.
الموضوعات
Protein Structure, Secondary , Support Vector Machine , Artificial Intelligence , Computational Biology/methodsالملخص
Polygonatum cyrtonema is a famous bulk medicinal material which is the medicinal and edible homologous. With the implementation of the traditional Chinese medicine industry to promote precise poverty alleviation, the planting area of P. cyrtonema in Jinzhai is becoming larger and larger in recent years. Jinzhai is located in the Dabie Mountainous area, which is the largest mountain area and county in Anhui Province. The cultivation of P. cyrtonema is scattered, and the traditional Chinese medicine resources investigation is not only inefficient and accurate. In this study,the "Resource 3"(ZY-3) remote sensing image was used as the best observation phase,and the method of support vector machine classification was used. The method of parallelepiped, minimum distance, mahalanob is distance, maximum likelihood classification and neural net were used to classify and recognize the P. cyrtonema in the whole region. In order to determine the accuracy and reliability of classification results, the accuracy of six supervised classification results was evaluated by confusion matrix method, and the advantages and disadvantages of six supervised classification methods for extracting P. cyrtonema field planting area were compared and analyzed. The results showed that the method of support vector machine classification was more appropriate than that using other classification methods. It provides a scientific basis for monitoring the planting area of P. cyrtonemain field.
الموضوعات
Medicine, Chinese Traditional , Polygonatum , Reproducibility of Results , Research Design , Support Vector Machineالملخص
In order to establish a rapid and non-destructive evaluation method for the identification of Armeniacae Semen Amarum and Persicae Semen from different origins, the spectral information of Armeniacae Semen Amarum and Persicae Semen in the range of 898-1 751 nm was collected based on hyperspectral imaging technology. Armeniacae Semen Amarum and Persicae Semen from different origins were collected as research objects, and a total of 720 Armeniacae Semen Amarum samples and 600 Persicae Semen samples were used for authenticity discrimination. The region of interest(ROI) and the average reflection spectrum in the ROI were obtained, followed by comparing five pre-processing methods. Then, partial least squares discriminant analysis(PLS-DA), support vector machine(SVM), and random forest(RF) method were established for classification models, which were evaluated by the confusion matrix of prediction results and receiver operating characteristic curve(ROC). The results showed that in the three sample sets, the se-cond derivative pre-processing method and PLS-DA were the best model combinations. The classification accuracy of the test set under the 5-fold cross-va-lidation was 93.27%, 96.19%, and 100.0%, respectively. It was consistent with the confusion matrix of the predicted results. The area under the ROC curve obtained the highest values of 0.992 3, 0.999 6, and 1.000, respectively. The study revealed that the near-infrared hyperspectral imaging technology could accurately identify the medicinal materials of Armeniacae Semen Amarum and Persicae Semen from different origins and distinguish the authentication of these two varieties.
الموضوعات
Drugs, Chinese Herbal , Hyperspectral Imaging , Least-Squares Analysis , Semen , Support Vector Machine , Technologyالملخص
Error self-detection based on error-related potentials (ErrP) is promising to improve the practicability of brain-computer interface systems. But the single trial recognition of ErrP is still a challenge that hinters the development of this technology. To assess the performance of different algorithms on decoding ErrP, this paper test four kinds of linear discriminant analysis algorithms, two kinds of support vector machines, logistic regression, and discriminative canonical pattern matching (DCPM) on two open accessed datasets. All algorithms were evaluated by their classification accuracies and their generalization ability on different sizes of training sets. The study results show that DCPM has the best performance. This study shows a comprehensive comparison of different algorithms on ErrP classification, which could give guidance for the selection of ErrP algorithm.
الموضوعات
Algorithms , Brain , Brain-Computer Interfaces , Discriminant Analysis , Electroencephalography , Support Vector Machineالملخص
OBJECTIVE@#According to the digital image features of corneal opacity, a multi classification model of support vector machine (SVM) was established to explore the objective quantification method of corneal opacity.@*METHODS@#The cornea digital images of dead pigs were collected, part of the color features and texture features were extracted according to the previous experience, and the SVM multi classification model was established. The test results of the model were evaluated by precision, sensitivity and @*RESULTS@#In the classification of corneal opacity, the highest @*CONCLUSIONS@#The SVM multi classification model can classify the degree of corneal opacity.
الموضوعات
Animals , Corneal Opacity , Support Vector Machine , Swineالملخص
Both feature representation and classifier performance are important factors that determine the performance of computer-aided diagnosis (CAD) systems. In order to improve the performance of ultrasound-based CAD for breast cancers, a novel multiple empirical kernel mapping (MEKM) exclusivity regularized machine (ERM) ensemble classifier algorithm based on self-paced learning (SPL) is proposed, which simultaneously promotes the performance of both feature representation and the classifier. The proposed algorithm first generates multiple groups of features by MEKM to enhance the ability of feature representation, which also work as the kernel transform in multiple support vector machines embedded in ERM. The SPL strategy is then adopted to adaptively select samples from easy to hard so as to gradually train the ERM classifier model with improved performance. This algorithm is verified on a B-mode ultrasound dataset and an elastography ultrasound dataset, respectively. The results show that the classification accuracy, sensitivity and specificity on B-mode ultrasound are (86.36±6.45)%, (88.15±7.12)%, and (84.52±9.38)%, respectively, and the classification accuracy, sensitivity and specificity on elastography ultrasound are (85.97±3.75)%, (85.93±6.09)%, and (86.03±5.88)%, respectively. It indicates that the proposed algorithm can effectively improve the performance of ultrasound-based CAD for breast cancers with the potential for application.
الموضوعات
Humans , Algorithms , Breast Neoplasms/diagnostic imaging , Computers , Diagnosis, Computer-Assisted , Support Vector Machine , Ultrasonographyالملخص
At present the prediction method of epilepsy patients is very time-consuming and vulnerable to subjective factors, so this paper presented an automatic recognition method of epilepsy electroencephalogram (EEG) based on common spatial model (CSP) and support vector machine (SVM). In this method, the CSP algorithm for extracting spatial characteristics was applied to the detection of epileptic EEG signals. However, the algorithm did not consider the nonlinear dynamic characteristics of the signals and ignored the time-frequency information, so the complementary characteristics of standard deviation, entropy and wavelet packet energy were selected for the combination in the feature extraction stage. The classification process adopted a new double classification model based on SVM. First, the normal, interictal and ictal periods were divided into normal and paroxysmal periods (including interictal and ictal periods), and then the samples belonging to the paroxysmal periods were classified into interictal and ictal periods. Finally, three categories of recognition were realized. The experimental data came from the epilepsy study at the University of Bonn in Germany. The average recognition rate was 98.73% in the first category and 99.90% in the second category. The experimental results show that the introduction of spatial characteristics and double classification model can effectively solve the problem of low recognition rate between interictal and ictal periods in many literatures, and improve the identification efficiency of each period, so it provides an effective detecting means for the prediction of epilepsy.
الموضوعات
Humans , Algorithms , Electroencephalography , Epilepsy/diagnosis , Signal Processing, Computer-Assisted , Support Vector Machineالملخص
Early accurate detection of inferior myocardial infarction is an important way to reduce the mortality from inferior myocardial infarction. Regrading the existing problems in the detection of inferior myocardial infarction, complex model structures and redundant features, this paper proposed a novel inferior myocardial infarction detection algorithm. Firstly, based on the clinic pathological information, the peak and area features of QRS and ST-T wavebands as well as the slope feature of ST waveband were extracted from electrocardiogram (ECG) signals leads Ⅱ, Ⅲ and aVF. In addition, according to individual features and the dispersion between them, we applied genetic algorithm to make judgement and then input the feature with larger degree into support vector machine (SVM) to realize the accurate detection of inferior myocardial infarction. The proposed method in this paper was verified by Physikalisch-Technische Bundesanstalt (PTB) diagnostic electrocardio signal database and the accuracy rate was up to 98.33%. Conforming to the clinical diagnosis and the characteristics of specific changes in inferior myocardial infarction ECG signal, the proposed method can effectively make precise detection of inferior myocardial infarction by morphological features, and therefore is suitable to be applied in portable devices development for clinical promotion.
الموضوعات
Humans , Algorithms , Databases, Factual , Electrocardiography , Inferior Wall Myocardial Infarction , Support Vector Machineالملخص
Fetal electrocardiogram signal extraction is of great significance for perinatal fetal monitoring. In order to improve the prediction accuracy of fetal electrocardiogram signal, this paper proposes a fetal electrocardiogram signal extraction method (GA-LSTM) based on genetic algorithm (GA) optimization with long and short term memory (LSTM) network. Firstly, according to the characteristics of the mixed electrocardiogram signal of the maternal abdominal wall, the global search ability of the GA is used to optimize the number of hidden layer neurons, learning rate and training times of the LSTM network, and the optimal combination of parameters is calculated to make the network topology and the mother body match the characteristics of the mixed signals of the abdominal wall. Then, the LSTM network model is constructed using the optimal network parameters obtained by the GA, and the nonlinear transformation of the maternal chest electrocardiogram signals to the abdominal wall is estimated by the GA-LSTM network. Finally, using the non-linear transformation obtained from the maternal chest electrocardiogram signal and the GA-LSTM network model, the maternal electrocardiogram signal contained in the abdominal wall signal is estimated, and the estimated maternal electrocardiogram signal is subtracted from the mixed abdominal wall signal to obtain a pure fetal electrocardiogram signal. This article uses clinical electrocardiogram signals from two databases for experimental analysis. The final results show that compared with the traditional normalized minimum mean square error (NLMS), genetic algorithm-support vector machine method (GA-SVM) and LSTM network methods, the method proposed in this paper can extract a clearer fetal electrocardiogram signal, and its accuracy, sensitivity, accuracy and overall probability have been better improved. Therefore, the method could extract relatively pure fetal electrocardiogram signals, which has certain application value for perinatal fetal health monitoring.
الموضوعات
Female , Humans , Pregnancy , Algorithms , Electrocardiography , Fetal Monitoring , Memory, Short-Term , Support Vector Machineالملخص
The automatic detection of arrhythmia is of great significance for the early prevention and diagnosis of cardiovascular diseases. Traditional arrhythmia diagnosis is limited by expert knowledge and complex algorithms, and lacks multi-dimensional feature representation capabilities, which is not suitable for wearable electrocardiogram (ECG) monitoring equipment. This study proposed a feature extraction method based on autoregressive moving average (ARMA) model fitting. Different types of heartbeats were used as model inputs, and the characteristic of fast and smooth signal was used to select the appropriate order for the arrhythmia signal to perform coefficient fitting, and complete the ECG feature extraction. The feature vectors were input to the support vector machine (SVM) classifier and K-nearest neighbor classifier (KNN) for automatic ECG classification. MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database were used to verify in the experiment. The experimental results showed that the feature engineering composed of the fitting coefficients of the ARMA model combined with the SVM classifier obtained a recall rate of 98.2% and a precision rate of 98.4%, and the