scholarly journals Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Suhuai Wang ◽  
Jingjie Li ◽  
Lin Sun ◽  
Jianing Cai ◽  
Shihui Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Artificial Neural Network ◽  
Ann Model ◽  
Training Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Testing Set

Abstract Background Early identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI). Methods A total of 2084 patients with acute myocardial infarction were enrolled in this study. (All data is available on Github: https://github.com/wangsuhuai/AMI-database1.git). The primary outcome is whether tachyarrhythmia occurred during admission containing atrial arrhythmia, ventricular arrhythmia, and supraventricular tachycardia. All data is randomly divided into a training set (80%) and an internal testing set (20%). Apply three machine learning algorithms: decision tree, random forest (RF), and artificial neural network (ANN) to learn the training set to build a model, then use the testing set to evaluate the prediction performance, and compare it with the model built by the Global Registry of Acute Coronary Events (GRACE) risk variable set. Results Three ML models predict the occurrence of tachyarrhythmias after AMI. After variable selection, the artificial neural network (ANN) model has reached the highest accuracy rate, which is better than the model constructed using the Grace variable set. After applying SHapley Additive exPlanations (SHAP) to make the model interpretable, the most important features are abnormal wall motion, lesion location, bundle branch block, age, and heart rate. Among them, RBBB (odds ratio [OR]: 4.21; 95% confidence interval [CI]: 2.42–7.02), ≥ 2 ventricular walls motion abnormal (OR: 3.26; 95% CI: 2.01–4.36) and right coronary artery occlusion (OR: 3.00; 95% CI: 1.98–4.56) are significant factors related to arrhythmia after AMI. Conclusions We used advanced machine learning methods to build prediction models for tachyarrhythmia after AMI for the first time (especially the ANN model that has the best performance). The current study can supplement the current AMI risk score, provide a reliable evaluation method for the clinic, and broaden the new horizons of ML and clinical research. Trial registration Clinical Trial Registry No.: ChiCTR2100041960.

scholarly journals Application of Machine Learning To Predict The Occurrence of Arrhythmia After Acute Myocardial Infarction

2021 ◽  
Author(s):  
suhuai Wang ◽  
jingjie Li ◽  
Lin Sun ◽  
Jianing Cai ◽  
Shihui Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Artificial Neural Network ◽  
Risk Score ◽  
Ann Model ◽  
Training Set ◽  
Artificial Neural ◽  
Testing Set

Abstract Background: Early identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI).Methods: A total of 2084 patients with acute myocardial infarction were enrolled in this study. The primary outcome is whether tachyarrhythmia occurred during admission containing atrial arrhythmia, ventricular arrhythmia, and supraventricular tachycardia. All data is randomly divided into training set (80%) and internal testing set (20%). Three machine learning algorithms (including decision tree, random forest, and artificial neural network) learn from the training set to build a model, use the testing set to evaluate the prediction performance, and compare it with the model built by the variable set involved GRACE risk score.Results：Three ML models predict the occurrence of tachyarrhythmia after AMI. After variable selection, the artificial neural network (ANN) model achieves the highest accuracy of 0.654 (95% CI, 0.625--0.683). The area under the value of the curve (AUC) is 0.597 (95% CI, 0.568-0.626). The highest accuracy of the model built using the Grace variable set is 0.627 (95% CI, 0.598-0.656), and the AUC value is 0.574 (95% CI, 0.545-0.603).Conclusions：We used advanced machine learning methods to build prediction models for tachyarrhythmia after AMI for the first time (especially the ANN model has the best performance). The current study can supplement the current AMI risk score, provide a reliable evaluation method for the clinic, and broaden the new horizons of ML and clinical research.Trial registration：Clinical Trial Registry No.: ChiCTR2100041960.

Prediction of Detonation Pressure of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 641-642 ◽  
pp. 460-463
Author(s):  
Yong Gang Liu ◽  
Xin Tian ◽  
Yue Qiang Jiang ◽  
Gong Bing Li ◽  
Yi Zhou Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Pressure ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network(ANN) model was constructed to predict the detonation pressure of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation pressure was used as output. The dataset of 41 aluminized explosives was randomly divided into a training set (30) and a prediction set (11). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [6–9–1], calculated detonation pressures show good agreement with experimental results. It is shown here that ANN is able to produce accurate predictions of the detonation pressure of aluminized explosive.

scholarly journals Artificial Neural Network-based Predicting the Risk of Complicating Ventricular Tachyarrhythmia After Acute Myocardial Infarction During Hospitalization

2020 ◽  
Author(s):  
Xifeng Zheng ◽  
Weidong Nong ◽  
Dehui Feng ◽  
Junxian Wang ◽  
Yan He
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Artificial Neural Network ◽  
Predictive Value ◽  
Confusion Matrix ◽  
Ventricular Tachyarrhythmia ◽  
Ann Model ◽  
Training Set

Abstract An artificial neural network (ANN) model was developed to predict the risks of complicating ventricular tachyarrhythmia (VTA) in patients with acute myocardial infarction (AMI). We enrolled information of 503 patients with 13 risk factors from the affiliated hospital of Guangdong medical university from January 2017 to December 2019. Risk factors were dimensionally reduced and simplified as new variables by principal component analysis (PCA). The cohort were randomly divided into a training set and a testing set at the ratio of 70%:30%. Training set was used to develop a model for the prediction of VTA while testing set was used to evaluate the performance of the model. Three new comprehensive variables by PCA are able to reflect all information of the original data. We determined the prediction model with optimizing parameters by cyclic searching which includes an input layer of three comprehensive variables, a single hidden layer composed of two neurons and a output layer. The area under curve (AUC) is 0.812 in training set and confusion matrix with accuracy 94.60%, sensitivity 63.04%, specificity 99.35%, positive predicative value 93.55%, negative predictive value 94.70%. The model displayed a decreased but medium discrimination with an AUC of 0.688 in the independent testing cohort, confusion matrix with accuracy 87.42%, Sensitivity 39.26%, specificity 98.37%, positive predicative value 84.62%, negative predictive value 87.68%. The research suggests that ANN model could be used to predict the risk of complicating ventricular tachyarrhythmia after acute myocardial infarction while should be further improved.

Prediction of Detonation Velocity of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 790 ◽  
pp. 673-676
Author(s):  
Yue Qiang Jiang ◽  
Yong Gang Liu ◽  
Xin Tian ◽  
Gong Bing Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Velocity ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network (ANN) model was constructed to predict the detonation velocity of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation velocity was used as output. The dataset of 61 aluminized explosives was randomly divided into a training set (49) and a prediction set (12). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [812, calculated detonation velocity show good agreement with experimental results. It is shown that ANN is able to produce accurate predictions of the detonation velocity of aluminized explosive.

scholarly journals Artificial Neural Network-Based Predicting the Risk of Complicating Ventricular Tachyarrhythmia after Acute Myocardial Infarction During Hospitalization

2020 ◽  
Author(s):  
Xifeng Zheng ◽  
Weidong Nong ◽  
Dehui Feng ◽  
Junxian Wang ◽  
Yan He
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Artificial Neural Network ◽  
Predictive Value ◽  
Confusion Matrix ◽  
Ventricular Tachyarrhythmia ◽  
Ann Model ◽  
Training Set

Abstract An artificial neural network (ANN) model was developed to predict the risks of complicating ventricular tachyarrhythmia (VTA) in patients with acute myocardial infarction (AMI). We enrolled information of 503 patients with 13 risk factors from the affiliated hospital of Guangdong medical university from January 2017 to December 2019. Risk factors were dimensionally reduced and simplified as new variables by principal component analysis (PCA). The cohort were randomly divided into a training set and a testing set at the ratio of 70%:30%. Training set was used to develop a model for the prediction of VTA while testing set was used to evaluate the performance of the model. Three new comprehensive variables by PCA are able to reflect all information of the original data. We determined the prediction model with optimizing parameters by cyclic searching which includes an input layer of three comprehensive variables, a single hidden layer composed of two neurons and a output layer. The area under curve (AUC) is 0.812 in training set and confusion matrix with accuracy 94.60%, sensitivity 63.04%, specificity 99.35%, positive predicative value 93.55%, negative predictive value 94.70%. The model displayed a decreased but medium discrimination with an AUC of 0.688 in the independent testing cohort, confusion matrix with accuracy 87.42%, Sensitivity 39.26%, specificity 98.37%, positive predicative value 84.62%, negative predictive value 87.68%. The research suggests that ANN model could be used to predict the risk of complicating ventricular tachyarrhythmia after acute myocardial infarction while should be further improved.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

scholarly journals Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network

2021 ◽  
Vol 03 (01) ◽  
pp. 45-52
Author(s):  
Hadjira Maouz ◽  
◽  
Asma Adda ◽  
Salah Hanini ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Aging ◽  
Training Data ◽  
Ethylene Propylene Diene Monomer ◽  
Ann Model ◽  
Ethylene Propylene ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer

scholarly journals Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik Terhadap Covid-19

2021 ◽  
Vol 5 (2) ◽  
pp. 109-118
Author(s):  
Euis Saraswati ◽  
Yuyun Umaidah ◽  
Apriade Voutama
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Artificial Neural Network ◽  
Text Mining ◽  
Ann Model ◽  
Rapid Spread ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
The Government ◽  
Artificial Neural Network Ann

Coronavirus disease (Covid-19) or commonly called coronavirus. This virus spreads very quickly and even almost infects the whole world, including Indonesia. A large number of cases and the rapid spread of this virus make people worry and even fear the increasing spread of the Covid-19 virus. Information about this virus has also been spread on various social media, one of which is Twitter. Various public opinions regarding the Covid-19 virus are also widely expressed on Twitter. Opinions on a tweet contain positive or negative sentiments. Sentiments of sentiment contained in a tweet can be used as material for consideration and evaluation for the government in dealing with the Covid-19 virus. Based on these problems, a sentiment analysis classification is needed to find out public opinion on the Covid-19 virus. This research uses Artificial Neural Network (ANN) algorithm with the Backpropagation method. The results of this test get 88.62% accuracy, 91.5% precision, and 95.73% recall. The results obtained show that the ANN model is quite good for classifying text mining.

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