جستجوی مقالات مرتبط با کلیدواژه "support vector machine" در نشریات گروه "پزشکی"

Accurate detection of Atrial Fibrillation (AF) has great significance in the field of medical science which can reduce the rate of mortality and morbidity. The present study focuses on Electrocardiography (ECG) signal classification using dimensionality reduction techniques combined with R wave to R wave interval (RR interval) features.

In the first approach, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), and Probabilistic Principal Component Analysis (PPCA) are performed independently on denoised ECG signal using Discrete Wavelet Transform (DWT) for the classification of ECG signal. In the second approach, the dimensionality reduction techniques combined with RR interval features are used for the classification of ECG signal.

Machine Learning (ML) algorithms such as Decision Tree (DT), Support Vector Machine (SVM), and Deep Learning (DL) algorithms such as Long Short Term Memory (LSTM) and Bi-Directional LSTM (BiLSTM) are used for classification purposes.

The proposed methodology provided an overall accuracy of 93.65% with PCA and LSTM classifier and an overall accuracy of 99.45% with PCA combined with RR interval features and LSTM classifier. The developed technology has potential applications in many practical solutions.

 Modeling with methods based on machine learning (ML) and artificial intelligence can help understand the complex relationships between ergonomic risk factors and employee health. The aim of this study was to use ML methods to estimate the effect of individual factors, ergonomic interventions, quality of work life (QWL), and productivity on work-related musculoskeletal disorders (WMSDs) in the neck area of office workers.

Study Design: 

A quasi-randomized control trial.

 To measure the impact of interventions, modeling with the ML method was performed on the data of a quasi-randomized control trial. The data included the information of 311 office workers (aged 32.04±5.34). Method neighborhood component analysis (NCA) was used to measure the effect of factors affecting WMSDs, and then support vector machines (SVMs) and decision tree algorithms were utilized to classify the decrease or increase of disorders.

 Three classified models were designed according to the follow-up times of the field study, with accuracies of 86.5%, 80.3%, and 69%, respectively. These models could estimate most influencer factors with acceptable sensitivity. The main factors included age, body mass index, interventions, QWL, some subscales, and several psychological factors. Models predicted that relative absenteeism and presenteeism were not related to the outputs.

 In this study, the focus was on disorders in the neck, and the obtained models revealed that individual and management interventions can be the main factors in reducing WMSDs in the neck. Modeling with ML methods can create a new understanding of the relationships between variables affecting WMSDs.

Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations.Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders.

We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders.

By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well.

Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other.

Independent Component Analysis (ICA) decomposition is a commonly used technique for eye blink artifact detection from Electroencephalogram (EEG) signals. Feature extraction from the decomposed ICs is a prime step for blink detection. This paper presents a new model of eye blink detection for ICA based approach, where the decomposed ICs are projected to their corresponding EEG segments (ReEEG), and feature extraction is performed on the ReEEG instead of the IC. ReEEG represents the eye blink activity more distinctly. Hence, ReEEG-based feature extraction is more potential in detecting eye blink artifacts than the traditional IC-based feature extraction.

This paper employs twelve EEG features to substantiate the superiority of ReEEG over IC. Support Vector Machine (SVM) is used as a classifier. A dataset, having 2638 clinical EEG epochs, is employed. All the considered twelve features are extracted from ReEEG and fed to SVM one at a time for blink detection. Then the obtained results are compared with an IC-based model with the same features.

The comparison reveals the success of the proposed ReEEG-based blink detection approach over the traditional IC-based approach. Accuracy, precision, recall, and f1 scores are calculated as performance measuring metrics. For almost all features, ReEEG-based approach achieved up to 12.25% higher accuracy, 24.95% higher precision, 13.49% higher recall, and 12.89% higher f1 score than the IC-based traditional method.

The proposed model will be useful for researchers in dealing with the eye blink artifacts of EEG signals with more efficacy.

Machine learning, especially deep convolutional neural networks (DCNNs), is a popular method for computerizing medical image analysis. This study aimed to develop DCNN models for histopathology image classification utilizing transfer learning.

We utilized 16 different pre-trained DCNNs to analyze the histopathology images from the animal diagnostic laboratory (ADL) database. During the image preprocessing stage, we applied two methods. The first method involved subtracting the mean of ImageNet images from all images. The second method involved subtracting the mean of histopathology training images from all images. Next, in the 16 pre-trained networks, feature extraction was done from their final six layers, and the features extracted from each layer were fed separately into the linear and non-linear support vector machine (SVM) for classification.

The results obtained from the ADL database show that the classification rate in lung tissue images is much better than that of the kidney and spleen. For example, the lowest detection rate in non-linear SVM for lung tissue is 14.96%, almost close to the highest accuracy in kidney and spleen tissue. The classification accuracy of the spleen images is better than that of the kidneys, with only a slight difference. In linear SVM on lung images, ResNet101 obtained the most accurate result with a value of 99.56%, followed by ResNet50, ResNet152, VGG_16, and VGG_19. In non-linear SVM on lung tissue images, the ResNet101 network with 99.65% and ResNet50 with 99.21%, followed by ResNet152, VGG_16, and VGG_19 obtained the highest detection rate.

The classification results obtained from different methods on the ADL (including kidney, spleen, and lung histopathology images) database, confirmed the validity of transferring knowledge between non-medical and medical histopathology images. Additionally, it demonstrates the success of combining classifiers trained on deep features. This research obtained higher accuracy in the ADL database than the works done.

Hypertension is associated with severe complications, and its detection is important to provide early information about a hypertension event, which is essential to prevent further complications. This study aimed to investigate a strategy for hypertension detection without a cuff using parameters of bioelectric signals, i.e., Electrocardiogram (ECG), Photoplethysmogram (PPG,) and an algorithm of Swarm-based Support Vector Machine (SSVM). This experimental study was conducted to develop a hypertension detection system. ECG and PPG bioelectrical records were collected from the Medical Information Mart for Intensive Care (MIMIC) from normal and hypertension participants and processed to find the parameters, used for the inputs of SSVM and comprised Pulse Arrival Time (PAT) and the characteristics of PPG signal derivatives. The SSVM was n Support Vector Machine (SVM) algorithm optimized using particle swarm optimization with Quantum Delta-potential-well (QDPSO). The SSVMs with different inputs were investigated to find the optimal detection performance. The proposed strategy was performed at 96% in terms of F1-score, accuracy, sensitivity, and specificity with better performance than the other methods tested and methods and also could develop a cuff-free hypertension monitoring system.  Hypertension using SSVM, ECG, and PPG parameters is acceptably performed. The hypertension detection had lower performance utilizing only PPG than both ECG and PPG.

In current study, we aimed to investigate if Attention Deficit Hyperactivity Disorder (ADHD) is better to be categorized among behavioral or neurodevelopmental disorders, based on some familial and environmental factors. 

We conducted correlation analysis to identify psychiatric disorders in the dataset which have an important impact on ADHD. Also, we used machine learning-based approaches combined with a feature selection algorithm to cluster and classify ADHD as a behavioral or neurodevelopmental disorder.

Model evaluation showed that ADHD is clustered in the group of behavioral disorders with the accuracy of 78%. Furthermore, Support Vector Machine (SVM) classified ADHD as a behavioral disorder with the accuracy of 72.66% and as a neurodevelopmental disorder with the accuracy of 60.07%.

In sum, we can say that our findings support categorizations systems like HiTOP in comparison to DSM-5. However, as biological factors were not included in our analysis, it should be considered with caution and examined in future researches.

Atrial fibrillation (AF) is a life threatening disease and can cause stroke, heart failure, and sometimes death. To reduce the rate of mortality and morbidity due to increased prevalence of AF, early detection of the same becomes a prior concern. Traditional machine learning (TML) algorithms and ensemble machine learning (EML) algorithms are proposed to detect AF in this article. The performances of both these methods are compared in this study. Methodology involves computation of RR interval features extracted from electrocardiogram and its classification into: normal, AF, and other rhythms. TML techniques such as Classification and Regression Tree, K Nearest Neighbor, C4.5, Iterative Dichotomiser 3, Support Vector Machine and EML classifier such as Random Forest (RF), and Rotation Forest are used for classification. The proposed method is evaluated using PhysioNet challenge 2017. During the tenfold cross validation, it is observed that RF classifier provided good classification accuracy of 99.10% with area under the curve of 0.998. Apart from contributing a new methodology, the proposed study also experimentally proves higher performance with ensemble learning method, RF. The methodology has many applications in health care management systems including defibrillators, cardiac pacemakers, etc.

Today, artificial intelligence is considered a powerful tool that can help physicians identify and diagnoseand predictdiseases. Gastric cancer has been the fourth most common malignancyand the second leading cause of cancer mortality in the world. Thus, timely diagnosis of this type of cancer could effectively control it. Thispaper compares AI(artificial intelligence)algorithms in diagnosing and predicting gastric cancer based on types of AI algorithms, sample size, accuracy,sensitivity, and specificity. This narrative-review paper aims to explore AI algorithms in diagnosingand predictinggastric cancer.To achieve this goal, we reviewed English articles published between 2011 and 2021 in PubMed and Science direct databases.According to the reviews conducted on the published papers, the endoscopic method has been the most used method to collect and incorporate samples into designed models. Also, the SVM(support vector machine), convolutional neural network (CNN), and deep-type CNN havebeen used the most; therefore,we propose the usage of these algorithms in medical subjects, especially in gastric cancer.

Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool, which makes the processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate.

In this paper, a hybrid approach based on deep features extracted from wavelet CNNs (WCNNs) weighted layers and multiclass support vector machine (MSVM) was proposed to improve the recognition of emotional states from electroencephalogram (EEG) signals. First, EEG signals were preprocessed and converted to Time-Frequency (T-F) color representation or scalogram using the continuous wavelet transform (CWT) method. Then, scalograms were fed into four popular pre-trained CNNs, AlexNet, ResNet-18, VGG-19, and Inception-v3 to fine-tune them. Then, the best feature layer from each one was used as input to the MSVM method to classify four quarters of the valence-arousal model. Finally, the subject-independent leave-one-subject-out criterion was used to evaluate the proposed method on DEAP and MAHNOB-HCI databases.

Results showed that extracting deep features from the earlier convolutional layer of ResNet-18 (Res2a) and classifying using the MSVM increased the average accuracy, precision, and recall by about 20% and 12% for MAHNOB-HCI and DEAP databases, respectively. Also, combining scalograms from four regions of pre-frontal, frontal, parietal, and parietal-occipital and two regions of frontal and parietal achieved the higher average accuracy of 77.47% and 87.45% for MAHNOB-HCI and DEAP databases, respectively.

Combining CNN and MSVM increased the recognition of emotion from EEG signals and the results were comparable to state-of-the art studies.

It could be beneficial to accelerate the hospitalization of patients with the identified clinical risk factorsof intensive care unit (ICU) admission, in order to control and reduce COVID-19-related mortality. This study aimedto determine the clinical risk factors associated with ICU hospitalization of COVID-19 patients.

The currentresearch was a cross-sectional study. The study recruited 7182 patients who had positive PCR tests between February 23,2020, and September 7, 2021 and were admitted to Afzalipour Hospital in Kerman, Iran, for at least 24 hours. Their demo-graphic characteristics, underlying diseases, and clinical parameters were collected. In order to analyze the relationshipbetween the studied variables and ICU admission, multiple logistic regression model, classification tree, and supportvector machine were used.

It was found that 14.7 percent (1056 patients) of the study participants were admit-ted to ICU. The patients’ average age was 51.25±21 years, and 52.8% of them were male. In the study, some factors suchas decreasing oxygen saturation level (OR=0.954, 95%CI: 0.944-0.964), age (OR=1.007, 95%CI: 1.004-1.011), respiratorydistress (OR=1.658, 95%CI: 1.410-1.951), reduced level of consciousness (OR=2.487, 95%CI: 1.721-3.596), hypertension(OR=1.249, 95%CI: 1.042-1.496), chronic pulmonary disease (OR=1.250, 95%CI: 1.006-1.554), heart diseases (OR=1.250,95%CI: 1.009-1.548), chronic kidney disease (OR=1.515, 95%CI: 1.111-2.066), cancer (OR=1.682, 95%CI: 1.130-2.505),seizures (OR=3.428, 95%CI: 1.615-7.274), and gender (OR=1.179, 95%CI: 1.028-1.352) were found to significantly affectICU admissions.

As evidenced by the obtained results, blood oxygen saturation level, the patient’s age, andtheir level of consciousness are crucial for ICU admission.

Estimating the probability of obstructive coronary artery disease in patients undergoing noncoronary cardiac surgery should be considered compulsory. Our study sought to evaluate the prevalence of obstructive coronary artery disease in patients undergoing valvular heart surgery and to utilize predictive methodology of concomitant obstructive coronary artery disease in these patients.

The retrospective study cohort was derived from a tertiary care hospital registry of patients undergoing coronary angiogram prior to valvular heart operations. Decision tree, logistic regression, and support vector machine models were built to predict the probability of the appearance of obstructive coronary artery disease. A total of 367 patients from 2016 to 2019 were analyzed.

The mean age of the study population was 57.3±9.3 years, 45.2% of the patients were male. Of 367 patients, 76 (21%) patients had obstructive coronary artery disease. The decision tree, logistics regression, and support vector machine models had an area under the curve of 72% (95% CI: 62% - 81%), 67% (95% CI: 56% - 77%), and 78% (95% CI: 68% - 87%), respectively. Multivariate analysis indicated that hypertension (OR 1.98; P=0.032), diabetes (OR 2.32; P=0.040), age (OR 1.05; P=0.006), and typical angina (OR 5.46; P<0.001) had significant role in predicting the presence of obstructive coronary artery disease.

Our study revealed that approximately one-fifth of patients who underwent valvular heart surgery had concomitant obstructive coronary artery disease. The support vector machine model showed the highest accuracy compared to the other model.

Medical tourism, one of the most profitable industries, has been growing rapidly in recent years. Especially Turkey, which has a high ranking among medical travel destinations, has some advantages that can become preferable for international patients. This study is among the first few studies which examine affecting factors in patients’ medical travel destination choices with Data Mining techniques. The data were obtained from patients who came to Ankara from abroad for treatment in May 2015 through a ques-tionnaire. Cross-industry Standard Process for data mining, known as the CRISP-DM method, is used in this study. After cleaning out the missing data, the models were created using classification algorithms. Models including Generalized Linear Model, Deep Learning, Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine (SVM) were compared, and SVM reached the best performance with 0.2% Relative Er-ror, 0.014 Root Mean Squared Error and 0.998 Correlation. As a result of the SVM model, effective attributes in patients’ satisfaction level include low price advantage, advertisement, doctors with high-quality education, trained assistant staff, relatives living in Turkey, and high technology of medical equipment, respectively. Special attention should be paid to these factors in developing plans and policies for the health tourism sector. However, the importance of related socio-demographic variables was indicated in detail. Eventually, some suggestions were presented to improve the weaknesses in the health tourism sector.