scholarly journals Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Junjun Chen ◽  
Hong Pu ◽  
Dianrong Wang
Keyword(s):  
Optimization Algorithm ◽  
Morphological Characteristics ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Cardiac Monitoring ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Cardiac Care ◽  
Public Data ◽  
Bioelectric Signals

This article first studied the morphological characteristics of the EEG for intensive cardiac care; that is, based on the analysis of the mechanism of disease diagnosis and treatment, a signal processing and machine learning model was constructed. Then, the methods of signal preprocessing, signal feature extraction, new neural network model structure, training mechanism, optimization algorithm, and efficiency are studied, and experimental verification is carried out for public data sets and clinical big data. Then, the principle of intensive cardiac monitoring, the mechanism of disease diagnosis, the types of arrhythmia, and the characteristics of the typical signal are studied, and the rhythm performance, individual variability, and neurophysiological basis of electrical signals in intensive cardiac monitoring are researched. Finally, the automatic signal recognition technology is studied. In order to improve the training speed and generalization ability, a multiclassification model based on Least Squares Twin Support Vector Machine (LS-TWIN-SVM) is proposed. The computational complexity of the classification model algorithm is compared, and intelligence is adopted. The optimization algorithm selects the parameters of the classifier and uses the EEG signal to simulate the model. Support Vector Machines and their improved algorithms have achieved the ultimum in shallow neural networks and have achieved good results in the classification and recognition of bioelectric signals. The LS-TWIN-SVM algorithm proposed in this paper has achieved good results in the classification and recognition of bioelectric signals. It can perform bioinformatics processing on intensive cardiac care EEG signals, systematically biometric information, diagnose diseases, the real-time detection, auxiliary diagnosis, and rehabilitation of patients.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

Labeling Privacy Protection SVM Using Privileged Information for COVID-19 Diagnosis

2022 ◽  
Vol 22 (3) ◽  
pp. 1-21
Author(s):  
Tongguang Ni ◽  
Jiaqun Zhu ◽  
Jia Qu ◽  
Jing Xue
Keyword(s):  
Privacy Protection ◽  
Local Area Network ◽  
Fog Computing ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Support Vector ◽  
Area Network ◽  
Privileged Information ◽  
Class Label ◽  
Healthcare Applications

Edge/fog computing works at the local area network level or devices connected to the sensor or the gateway close to the sensor. These nodes are located in different degrees of proximity to the user, while the data processing and storage are distributed among multiple nodes. In healthcare applications in the Internet of things, when data is transmitted through insecure channels, its privacy and security are the main issues. In recent years, learning from label proportion methods, represented by inverse calibration (InvCal) method, have tried to predict the class label based on class label proportions in certain groups. For privacy protection, the class label of the sample is often sensitive and invisible. As a compromise, only the proportion of class labels in certain groups can be used in these methods. However, due to their weak labeling scheme, their classification performance is often unsatisfactory. In this article, a labeling privacy protection support vector machine using privileged information, called LPP-SVM-PI, is proposed to promote the accuracy of the classifier in infectious disease diagnosis. Based on the framework of the InvCal method, besides using the proportion information of the class label, the idea of learning using privileged information is also introduced to capture the additional information of groups. The slack variables in LPP-SVM-PI are represented as correcting function and projected into the correcting space so that the hidden information of training samples in groups is captured by relaxing the constraints of the classification model. The solution of LPP-SVM-PI can be transformed into a classic quadratic programming problem. The experimental dataset is collected from the Coronavirus disease 2019 (COVID-19) transcription polymerase chain reaction at Hospital Israelita Albert Einstein in Brazil. In the experiment, LPP-SVM-PI is efficiently applied for COVID-19 diagnosis.

scholarly journals Emphasis Learning, Features Repetition in Width Instead of Length to Improve Classification Performance: Case Study—Alzheimer’s Disease Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 941
Author(s):  
Hamid Akramifard ◽  
MohammadAli Balafar ◽  
SeyedNaser Razavi ◽  
Abd Rahman Ramli
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Principal Component ◽  
Classification Problem ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Support Vector ◽  
Svm Classifier ◽  
Small Subset ◽  
Alzheimer’S Disease Diagnosis

In the past decade, many studies have been conducted to advance computer-aided systems for Alzheimer’s disease (AD) diagnosis. Most of them have recently developed systems concentrated on extracting and combining features from MRI, PET, and CSF. For the most part, they have obtained very high performance. However, improving the performance of a classification problem is complicated, specifically when the model’s accuracy or other performance measurements are higher than 90%. In this study, a novel methodology is proposed to address this problem, specifically in Alzheimer’s disease diagnosis classification. This methodology is the first of its kind in the literature, based on the notion of replication on the feature space instead of the traditional sample space. Briefly, the main steps of the proposed method include extracting, embedding, and exploring the best subset of features. For feature extraction, we adopt VBM-SPM; for embedding features, a concatenation strategy is used on the features to ultimately create one feature vector for each subject. Principal component analysis is applied to extract new features, forming a low-dimensional compact space. A novel process is applied by replicating selected components, assessing the classification model, and repeating the replication until performance divergence or convergence. The proposed method aims to explore most significant features and highest-preforming model at the same time, to classify normal subjects from AD and mild cognitive impairment (MCI) patients. In each epoch, a small subset of candidate features is assessed by support vector machine (SVM) classifier. This repeating procedure is continued until the highest performance is achieved. Experimental results reveal the highest performance reported in the literature for this specific classification problem. We obtained a model with accuracies of 98.81%, 81.61%, and 81.40% for AD vs. normal control (NC), MCI vs. NC, and AD vs. MCI classification, respectively.

scholarly journals Differential Evolution Tuned Support Vector Machine for Autistic Spectrum Disorder Diagnosis

2019 ◽  
Vol 8 (2) ◽  
pp. 3861-3870
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Optimization Algorithm ◽  
Autistic Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Classification Model ◽  
Support Vector ◽  
Data Set ◽  
Autistic Spectrum

Autistic Spectrum Disorder (ASD) is a brain developmental disorder which weakens the ability to communicate and interact with others. A child with autism spectrum disorder may have different, repetitive patterns of behaviour, interests or activities, including some specific signs. To diagnose the behaviour of ASD and identify the level of disease on the human is still a challenging task for the doctors. Only by the trained and experienced physician can identify the ASD immediately. The data set for autism problem consist of number of causes and the results based on the symptoms for ASD. So, Data mining algorithm is in need to organize and pattern the ASD details. The machine algorithms are available to classify the data in data mining works. In this proposed work, a machine learning algorithm called Support Vector Machine is used to classify the ASD children accurately. SVM is one of the classification algorithms which finding the hyper plane that maximizes the margin between the two classes. Though SVM give better identification of disease, some children have their unique nature which hides their problem of ASD easily. So, to diagnose the problem accurately, the user defined SVM parameters are tuned by optimization algorithm called Differential Evolutionary Algorithm. DE is an optimization algorithm used to find the optimal solution of SVM parameters. Further, to improve the performance of the proposed method, the dimension reduction technique is followed to reduce the SVM and ANN network dimension. The Sequential Feature Selection (SFS) method is applied in this paper, which select the most influenced variables for the output. The reduced network is further classified by ANN and SVM model. The Data set for the ANN and SVM network has been taken from the real records of the multi-specialty hospitals. The SVM and DE optimized SVM results are compared with another classification model called Artificial Neural Networks. The test results show the betterment of DE optimized SVM which give the classification of ASD child very accurately compare with ANN and DE optimized ANN.

scholarly journals Heart Disease Prediction Using Glowworm Swarm Optimization and Support Vector Machine Classifier

2022 ◽  
Vol 16 ◽  
pp. 38-45
Author(s):  
O. , Bhaskaru ◽  
M. Sreedevi
Keyword(s):  
Support Vector Machine ◽  
Heart Disease ◽  
Optimization Algorithm ◽  
True Positive Rate ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Feature Based ◽  
Heart Disease Diagnosis

At present, health disorder is growing day by way of the day due to existence lifestyle, hereditary. Particularly, heart disease has ended up greater frequent these days. Heart disorder prognosis technique is very quintessential and integral trouble for the patient's health. Besides, it will help out to limit disorder to a larger distinctive level. The role of using strategy like machine learning and algorithm such as heart disease diagnosis using Data Mining(DM) techniques is very significant. In the previous system, the Fuzzy Extreme Learning Machine (FELM) was proposed to predict heart disease, ensuring an accurate and timely diagnosis. However, it only achieves 87.14 % of accuracy. To improve the classification accuracy, the proposed system designed an Improved Step Adjustment based Glowworm Swarm Optimization Algorithm with Weighted Feature based Support Vector Machine (ISAGSO-WFSVM) for Heart disease diagnosis. This proposed venture utilizes the dataset of heart disease for input. Using the Improved Step Adjustment based Glowworm Swarm Optimization Algorithm (ISAGSO) to enhance the true positive rate, optimal features are then selected. Finally, with the aid of the Weighted Feature based Support Vector Machine (WFSVM) classifier, classification is carried out relying selected features. In the proposed method, better performance obtained and that is validated through the experimental results in terms of precision, accuracy, recall and f-measures

scholarly journals Comparative analysis of classification algorithms for chronic kidney disease diagnosis

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Zainuri Saringat ◽  
Aida Mustapha ◽  
R. D. Rohmat Saedudin ◽  
Noor Azah Samsudin
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Supervised Classification ◽  
Bone Fractures ◽  
Kidney Diseases ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Support Vector ◽  
Classification Algorithms ◽  
Nearest Neighbour

Chronic Kidney Disease (CKD) is one of the leading cause of death contributed by other illnesses such as diabetes, hypertension, lupus, anemia or weak bones that lead to bone fractures. Early prediction of CKD is important in order to contain the disesase. However, instead of predicting the severity of CKD, the objective of this paper is to predict the diagnosis of CKD based on the symptoms or attributes observed in a particular case, whether the stage is acute or chronic. To achieve this, a classification model is proposed to label stage of severity for kidney diseases patients. The experiments then investigated the performance of the proposed classification model based on eight supervised classification algorithms, which are ZeroR, Rule Induction, Support Vector Machine, Naïve Bayes, Decision Tree, Decision Stump, k-Nearest Neighbour, and Classification via Regression. The performance of the all classifiers is evaluated based on accuracy, precision, and recall. The results showed that the regression classifier perform best in the kidney diagnostic procedure.

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