scholarly journals Abstract P051: Application Of Artificial Intelligence In Transcriptome-based Diagnosis Of Cardiomyopathies

Hypertension ◽  
2020 ◽  
Vol 76 (Suppl_1) ◽  
Author(s):  
Xi Cheng ◽  
Ahmad Alimadadi ◽  
Ishan Manandhar ◽  
Sachin Aryal ◽  
Patricia B Munroe ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Care ◽  
Principal Component ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Diagnostic Strategy ◽  
Novel Approach ◽  
Increased Risk ◽  
Different Types ◽  
Pathway Analyses

Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are predisposing conditions for increased risk of cardiovascular diseases including heart failure, valve disease and arrhythmias. An accurate diagnostic strategy of differentially classifying different types of cardiomyopathies could contribute to precision medicine in routine clinical care. We hypothesized that artificial intelligence (AI) could be trained with cardiac transcriptomic data for diagnostic classifications of clinical cardiomyopathies. To test this hypothesis, various supervised machine learning (ML) models, such as support vector machine with radial kernel (svmRadial), neural networks with principal component analysis (pcaNNet), decision tree (DT), elastic net (ENet) and random forest (RF), were used to analyze RNA-seq data of human left ventricle tissues collected from 41 DCM patients, 47 ICM patients and 49 non-failure controls (NF). Initial ML classifications achieved an AUC of ~0.96 (ENet, pcaNNet and svmRadial) for NF vs DCM, an AUC of ~0.89 (RF) for NF vs ICM, and an AUC of ~0.90 (svmRadial) for DCM vs ICM. Next, 50 highly contributing genes (HCGs) for classifying NF and DCM, 68 HCGs for classifying NF and ICM, and 59 HCGs for classifying DCM and ICM were selected for re-training ML models. The re-trained models achieved an AUC of ~0.96 (RF) for NF vs DCM, an AUC of ~0.97 (pcaNNet) for NF vs ICM, and an AUC of ~0.94 (RF) for DCM vs ICM. Pathway analyses of the selected HCGs further demonstrated their pathophysiological roles in cardiovascular dysfunctions including cardiomyopathies. Overall, our study demonstrates the promising potential of using AI via ML models as a novel approach to achieve a greater level of precision in diagnosing different types of clinical cardiomyopathies.

scholarly journals Machine learning-based classification and diagnosis of clinical cardiomyopathies

2020 ◽  
Vol 52 (9) ◽  
pp. 391-400 ◽  
Author(s):  
Ahmad Alimadadi ◽  
Ishan Manandhar ◽  
Sachin Aryal ◽  
Patricia B. Munroe ◽  
Bina Joe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Principal Component ◽  
Support Vector ◽  
Rna Seq ◽  
Novel Approach ◽  
Different Types ◽  
Human Left Ventricle ◽  
Pathway Analyses ◽  
Radial Kernel

Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common types of cardiomyopathies leading to heart failure. Accurate diagnostic classification of different types of cardiomyopathies is critical for precision medicine in clinical practice. In this study, we hypothesized that machine learning (ML) can be used as a novel diagnostic approach to analyze cardiac transcriptomic data for classifying clinical cardiomyopathies. RNA-Seq data of human left ventricle tissues were collected from 41 DCM patients, 47 ICM patients, and 49 nonfailure controls (NF) and tested using five ML algorithms: support vector machine with radial kernel (svmRadial), neural networks with principal component analysis (pcaNNet), decision tree (DT), elastic net (ENet), and random forest (RF). Initial ML classifications achieved ~93% accuracy (svmRadial) for NF vs. DCM, ~82% accuracy (RF) for NF vs. ICM, and ~80% accuracy (ENet and svmRadial) for DCM vs. ICM. Next, 50 highly contributing genes (HCGs) for classifying NF and DCM, 68 HCGs for classifying NF and ICM, and 59 HCGs for classifying DCM and ICM were selected for retraining ML models. Impressively, the retrained models achieved ~90% accuracy (RF) for NF vs. DCM, ~90% accuracy (pcaNNet) for NF vs. ICM, and ~85% accuracy (pcaNNet and RF) for DCM vs. ICM. Pathway analyses further confirmed the involvement of those selected HCGs in cardiac dysfunctions such as cardiomyopathies, cardiac hypertrophies, and fibrosis. Overall, our study demonstrates the promising potential of using artificial intelligence via ML modeling as a novel approach to achieve a greater level of precision in diagnosing different types of cardiomyopathies.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

scholarly journals Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models

2020 ◽  
Vol 49 (5) ◽  
pp. 20190441 ◽  
Author(s):  
Hakan Amasya ◽  
Derya Yildirim ◽  
Turgay Aydogan ◽  
Nazan Kemaloglu ◽  
Kaan Orhan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cervical Vertebrae ◽  
Clinical Decision ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Human Observer ◽  
Ann Model ◽  
Visual Evaluation ◽  
Cervical Vertebral Maturation

Objectives: This study aimed to develop five different supervised machine learning (ML) classifier models using artificial intelligence (AI) techniques and to compare their performance for cervical vertebral maturation (CVM) analysis. A clinical decision support system (CDSS) was developed for more objective results. Methods: A total of 647 digital lateral cephalometric radiographs with visible C2, C3, C4 and C5 vertebrae were chosen. Newly developed software was used for manually labelling the samples, with the integrated CDSS developed by evaluation of 100 radiographs. On each radiograph, 26 points were marked, and the CDSS generated a suggestion according to the points and CVM analysis performed by the human observer. For each sample, 54 features were saved in text format and classified using logistic regression (LR), support vector machine, random forest, artificial neural network (ANN) and decision tree (DT) models. The weighted κ coefficient was used to evaluate the concordance of classification and expert visual evaluation results. Results: Among the CVM stage classifier models, the best result was achieved using the ANN model (κ = 0.926). Among cervical vertebrae morphology classifier models, the best result was achieved using the LR model (κ = 0.968) for the presence of concavity, and the DT model (κ = 0.949) for vertebral body shapes. Conclusions: This study has proposed ML models for CVM assessment on lateral cephalometric radiographs, which can be used for the prediction of cervical vertebrae morphology. Further studies should be done especially of forensic applications of AI models through CVM evaluations.

scholarly journals A Novel Approach for Efficient Forgery Image Detection Using Hybrid Feature Extraction and Classification

2018 ◽  
Vol 7 (3.27) ◽  
pp. 215
Author(s):  
G Clara Shanthi ◽  
V Cyril Raj
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Second Stage ◽  
Image Forgery Detection ◽  
Novel Approach ◽  
Different Types ◽  
Hybrid Feature Extraction

Image forgery detection is developing as one of the major research topic among researchers in the area of image forensics. These image forgery detection is addressed by two different types: (i) Active, (ii) Passive. Further consist of some different methods, such as Copy-Move, Image Splicing, and Retouching. Development of the image forgery is very necessary to detect as the image is true or it is forgery. In this paper, an efficient forgery detection and classification technique is proposed by three different stages. At first stage, preprocessing is carried out using bilateral filtering to remove noise. At second stage, extract unique features from forged image by using efficient feature extraction technique namely Gray Level Co-occurance Matrices (GLCM). Here, the GLCM improves the feature extraction accuracy. Finally, forged image is detected by classifying the type of image forgery using Multi Class- Support Vector Machine (SVM). Also, the performance of the proposed method is analyzed using the following metrics: accuracy, sensitivity and specificity.  

scholarly journals Hyperspectral image classification using support vector machines

2020 ◽  
Vol 9 (4) ◽  
pp. 684
Author(s):  
Jonnadula Dr.J.Harikiran Harikiran
Keyword(s):  
Support Vector Machines ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Bidimensional Empirical Mode Decomposition

In this paper, a novel approach for hyperspectral image classification technique is presented using principal component analysis (PCA), bidimensional empirical mode decomposition (BEMD) and support vector machines (SVM). In this process, using PCA feature extraction technique on Hyperspectral Dataset, the first principal component is extracted. This component is supplied as input to BEMD algorithm, which divides the component into four parts, the first three parts represents intrensic mode functions (IMF) and last part shows the residue. These BIMFs and residue image is further taken as input to the SVM for classification. The results of experiments on two popular datasets of hyperspectral remote sensing scenes represent that the proposed-model offers a competitive analyticalperformance in comparison to some established methods.

scholarly journals Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

2021 ◽  
Vol 10 (22) ◽  
pp. 5330
Author(s):  
Francesco Paolo Lo Muzio ◽  
Giacomo Rozzi ◽  
Stefano Rossi ◽  
Giovanni Battista Luciani ◽  
Ruben Foresti ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Intraoperative Imaging ◽  
Complex Function ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Open Chest ◽  
Chest Surgery

The human right ventricle is barely monitored during open-chest surgery due to the absence of intraoperative imaging techniques capable of elaborating its complex function. Accordingly, artificial intelligence could not be adopted for this specific task. We recently proposed a video-based approach for the real-time evaluation of the epicardial kinematics to support medical decisions. Here, we employed two supervised machine learning algorithms based on our technique to predict the patients’ outcomes before chest closure. Videos of the beating hearts were acquired before and after pulmonary valve replacement in twelve Tetralogy of Fallot patients and recordings were properly labeled as the “unhealthy” and “healthy” classes. We extracted frequency-domain-related features to train different supervised machine learning models and selected their best characteristics via 10-fold cross-validation and optimization processes. Decision surfaces were built to classify two additional patients having good and unfavorable clinical outcomes. The k-nearest neighbors and support vector machine showed the highest prediction accuracy; the patients’ class was identified with a true positive rate ≥95% and the decision surfaces correctly classified the additional patients in the “healthy” (good outcome) or “unhealthy” (unfavorable outcome) classes. We demonstrated that classifiers employed with our video-based technique may aid cardiac surgeons in decision making before chest closure.

scholarly journals A Fusion-Based Hybrid-Feature Approach for Recognition of Unconstrained Offline Handwritten Hindi Characters

2021 ◽  
Vol 13 (9) ◽  
pp. 239
Author(s):  
Danveer Rajpal ◽  
Akhil Ranjan Garg ◽  
Om Prakash Mahela ◽  
Hassan Haes Alhelou ◽  
Pierluigi Siano
Keyword(s):  
Character Recognition ◽  
Large Population ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Hybrid Features ◽  
Efficient Management ◽  
Novel Approach ◽  
Proposed Model ◽  
Machine Learning Approach

Hindi is the official language of India and used by a large population for several public services like postal, bank, judiciary, and public surveys. Efficient management of these services needs language-based automation. The proposed model addresses the problem of handwritten Hindi character recognition using a machine learning approach. The pre-trained DCNN models namely; InceptionV3-Net, VGG19-Net, and ResNet50 were used for the extraction of salient features from the characters’ images. A novel approach of fusion is adopted in the proposed work; the DCNN-based features are fused with the handcrafted features received from Bi-orthogonal discrete wavelet transform. The feature size was reduced by the Principal Component Analysis method. The hybrid features were examined with popular classifiers namely; Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). The recognition cost was reduced by 84.37%. The model achieved significant scores of precision, recall, and F1-measure—98.78%, 98.67%, and 98.69%—with overall recognition accuracy of 98.73%.

Somatic Cells Recognition by Application of Gabor Feature-Based (2D)2PCA

2017 ◽  
Vol 31 (12) ◽  
pp. 1757009 ◽  
Author(s):  
Xiaojing Gao ◽  
Heru Xue ◽  
Xin Pan ◽  
Xinhua Jiang ◽  
Yanqing Zhou ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Somatic Cells ◽  
Bovine Mastitis ◽  
Principal Component ◽  
Feature Space ◽  
Support Vector ◽  
Large Set ◽  
Novel Approach ◽  
Gabor Feature ◽  
Feature Based

In this paper, we propose a novel approach of Gabor feature based on bi-directional two-dimensional principal component analysis ((2D)2PCA) for somatic cells recognition. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)2PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. The experimental results are obtained using a large set of images from different sources. The results of our proposed method are not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy.

Distinguishing the Representative Marshes in China Based on Artificial Intelligence

2017 ◽  
Vol 33 (4) ◽  
pp. 471-476
Author(s):  
Yongming Chen ◽  
Ping Lin
Keyword(s):  
Artificial Intelligence ◽  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Marsh Habitat ◽  
Principal Component Analysis Algorithm ◽  
True Color

Abstract. A new means to distinguish the habitat spectacles of the representative marshes in China based on artificial intelligence is presented in this article. Three typical instances including Yancheng mudflat marsh, Zoige plateau marsh, and Dongzhai Harbor mangrove forest marsh were investigated. Firstly, the RGB true-color pictures of the marsh habitat spectacles were resized to the appropriate sizes and switched to gray intensity pictures. Secondly, the GIST descriptors were evaluated for encoding the marsh habitat spectacles at both a basic level and a superordinate level. Thirdly, the principal component analysis algorithm was performed to extract the principal components from the encoded features. Finally, the multi-class support vector machine (MSVM) algorithm was used to discriminate the marsh habitat spectacles using the principal components. The recognition percisions for the training and test set reached 72.5% and 70.6%, respectively. It was accounted that the proposed methods could be applied to distinguishing the representative marsh habitat spectacles in China. Keywords: Classification, GIST descriptiors, Marsh Habitat spectacle, Multi-class support vector machine, Principal component analysis.

scholarly journals Comparing different supervised machine learning algorithms for disease prediction

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Shahadat Uddin ◽  
Arif Khan ◽  
Md Ekramul Hossain ◽  
Mohammad Ali Moni
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Relative Performance ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Disease Prediction ◽  
Machine Learning Algorithm ◽  
Different Types

Abstract Background Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study aims to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction. Methods In this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction. Results We found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered. Conclusion This study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.

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