scholarly journals Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 207
Author(s):  
Asma Baccouche ◽  
Begonya Garcia-Zapirain ◽  
Cristian Castillo Olea ◽  
Adel Elmaghraby
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Ensemble Learning ◽  
Heart Diseases ◽  
Hypertensive Heart Disease ◽  
Network Models ◽  
Features Selection ◽  
Neural Network Models ◽  
Learning Framework ◽  
Different Types

Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

A Confidence-Based RBF Neural Network Ensemble Learning Paradigm with Application to Delinquent Prediction for Credit Risk Management

2010 ◽  
pp. 105-117
Author(s):  
Lean Yu ◽  
Shouyang Wang
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Credit Risk ◽  
Ensemble Learning ◽  
Rbf Neural Network ◽  
Network Models ◽  
Neural Network Ensemble ◽  
Credit Risk Management ◽  
Learning Paradigm ◽  
Neural Network Models

In this study, a multistage confidence-based radial basis function (RBF) neural network ensemble learning model is proposed to design a reliable delinquent prediction system for credit risk management. In the first stage, a bagging sampling approach is used to generate different training datasets. In the second stage, the RBF neural network models are trained using various training datasets from the previous stage. In the third stage, the trained RBF neural network models are applied to the testing dataset and some prediction results and confidence values can be obtained. In the fourth stage, the confidence values are scaled into a unit interval by logistic transformation. In the final stage, the multiple different RBF neural network models are fused to obtain the final prediction results by means of confidence measure. For illustration purpose, two publicly available credit datasets are used to verify the effectiveness of the proposed confidence-based RBF neural network ensemble learning paradigm.

scholarly journals Use of Modular Neural Network for Heart Disease

2010 ◽  
pp. 196-201
Author(s):  
Harsh Vazirani ◽  
Rahul Kala ◽  
Anupam Shukla ◽  
Ritu Tiwari
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Network Models ◽  
Training Data ◽  
Neural Network Models ◽  
Modular Neural Network ◽  
Medical Disease ◽  
Research Areas ◽  
Product Method ◽  
Testing Accuracy

The medical field is very versatile field and one of the interested research areas for the scientist. It deals with many medical disease problems starting with the diagnosis of the disease, preventing from the disease and treatment for the disease. There are various types of medical disease and accordingly various types of treatment methods. In this paper we mostly concern about the diagnosis of the heart disease. Mainly two types of the diagnosis method are used one is manual and other is automatic diagnosis which consists of diagnosis of disease with the help of intelligent expert system. In this paper the modular neural network is used to diagnosis the heart disease. The attributes are divided and given to the two neural network models Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN) for training and testing. The two integration techniques are used two integrate the results and provide the final training accuracy and testing accuracy. The modular neural network with probabilistic product method gave an accuracy of 87.02% over training data and 85.88% over testing accuracy and with probabilistic product method gave an accuracy of 89.72% over training data and 84.70% over testing accuracy, which was experimentally determined to be better than monolithic neural networks.

scholarly journals Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

10.2196/16374 ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. e16374
Author(s):  
Subendhu Rongali ◽  
Adam J Rose ◽  
David D McManus ◽  
Adarsha S Bajracharya ◽  
Alok Kapoor ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Logistic Regression ◽  
Intensive Care ◽  
Logistic Regression Model ◽  
Outcome Prediction ◽  
Network Models ◽  
Neural Network Models ◽  
Latent Space ◽  
Different Types

Background Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications). Objective This study aimed to model such relations and build predictive models using the EHR data from intensive care units. We developed innovative neural network models and compared them with the widely used logistic regression model and other state-of-the-art neural network models to predict the patient’s mortality using their longitudinal EHR data. Methods We built a set of neural network models that we collectively called as long short-term memory (LSTM) outcome prediction using comprehensive feature relations or in short, CLOUT. Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinical features during a patient’s encounter and integrate the latent representation into an LSTM-based predictive model framework. In addition, we designed an ablation experiment to identify risk factors from our CLOUT models. Using physicians’ input as the gold standard, we compared the risk factors identified by both CLOUT and logistic regression models. Results Experiments on the Medical Information Mart for Intensive Care-III dataset (selected patient population: 7537) show that CLOUT (area under the receiver operating characteristic curve=0.89) has surpassed logistic regression (0.82) and other baseline NN models (<0.86). In addition, physicians’ agreement with the CLOUT-derived risk factor rankings was statistically significantly higher than the agreement with the logistic regression model. Conclusions Our results support the applicability of CLOUT for real-world clinical use in identifying patients at high risk of mortality.

Artificial neural network models for diagnosing heart disease: a brief review

2014 ◽  
Vol 6 (3) ◽  
pp. 73-78 ◽  
Author(s):  
Behnam Zebardast ◽  
Rahim Rashidi ◽  
Taha Hasanpour ◽  
Farhad Soleimanian Gharehchopogh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Network Models ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Models

scholarly journals Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers

2020 ◽  
Vol 34 (04) ◽  
pp. 5810-5817
Author(s):  
Masoumeh Soflaei ◽  
Hongyu Guo ◽  
Ali Al-Bashabsheh ◽  
Yongyi Mao ◽  
Richong Zhang
Keyword(s):  
Neural Network ◽  
Vector Quantization ◽  
Rate Distortion ◽  
Network Models ◽  
Representation Learning ◽  
Classification Problem ◽  
Neural Network Models ◽  
Rate Distortion Theory ◽  
Learning Framework ◽  
Neural Network Classifiers

We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation learning problem, which we call “IB learning”. We show that IB learning is, in fact, equivalent to a special class of the quantization problem. The classical results in rate-distortion theory then suggest that IB learning can benefit from a “vector quantization” approach, namely, simultaneously learning the representations of multiple input objects. Such an approach assisted with some variational techniques, result in a novel learning framework, “Aggregated Learning”, for classification with neural network models. In this framework, several objects are jointly classified by a single neural network. The effectiveness of this framework is verified through extensive experiments on standard image recognition and text classification tasks.

scholarly journals Neural Network Models as Evidence for Different Types of Visual Representations

1995 ◽  
Vol 19 (4) ◽  
pp. 575-579 ◽  
Author(s):  
Stephen M. Kosslyn ◽  
Christopher F. Chabris ◽  
David P. Baker
Keyword(s):  
Neural Network ◽  
Visual Representations ◽  
Network Models ◽  
Neural Network Models ◽  
Different Types
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