Analysis of Neural Networks Based Heart Disease Prediction System

Rajamhoana, SP; Devi, C. Akalya; Umamaheswari, K.; Kiruba, R Judith; Karunya, K.; Deepika, R.

doi:10.1109/hsi.2018.8431153

Cited by 53 publications

(13 citation statements)

References 10 publications

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“…Deep learning techniques have played a significant role in the healthcare domain for knowledge discovery and diseases classification, like heart disease, diabetes, and brain disease, using the collected biomedical data as surveyed in [15,16], that showed several types of clinical applications using the deep learning framework, and also noted some limitations and needs of improvements. In particular, several predictive models based on neural networks have been designed for accurately classifying heart disease [17]. In recent works, convolutional neural networks (CNN) have been implemented for identifying different categories of heartbeats in ECG signals [18], and a modified deep convolutional neural network has been utilized for classifying the ECG data into normal and abnormal [19].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…The neural network [11]technique is a collection of nodes where every node is associated with their respective weights. Nodes calculate sum of weights and it forwards to the activation function.…”

Section: Neural Networkmentioning

confidence: 99%

Heart Disease Prediction using Machine Learning Techniques

Yaswanth*,

Riyazuddin

2020

IJITEE

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Recent advancement of technology allows the automation of things to be done using machine learning techniques. These machine learning techniques can also be used for detecting or predicting the heart disease in the early phase. The health care industry produces a huge amount of data which is in unstructured manner that cannot be understood by a machine. Due to development of modern technology, health care industries also managing the data in a structured manner which can be understood by machine learning technology. In this environment if we use machine learning algorithms for prediction of heart disease, then there is a chance to detect the heart disease status in the early phase and to alert patient to get a better treatment to cure that disease. This paper implements seven supervised learning algorithms which are KNN, Decision Tree, Naive Bayes, Logistic Regression, Random Forest, Support Vector Machine and Neural Networks for heart disease prediction. This paper generates algorithm performance metrics like Accuracy, Precision, Recall, F-score and ROC values for how the system was predicting accurately. In this paper among those seven algorithms, Neural Networks gave best accuracy as 92.30% and this system provides experimental results for how the model is accurate for heart disease prediction.

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“…In general, most of the literature on using machine learning for heart disease diagnosis utilized two major techniques: the ANN [16,17,18,19] and the SVM [20,21,22,23]. They both have high classification accuracy, but they suffer from low learning speed when the number of instances or the number of features is huge.…”

Section: Dis-cat3mentioning

confidence: 99%

“…A few modification have been applied to equation (13) by introducing a new term, the feature count ratio , see equations (16) and (17). To further enhance the proportional relation between FRC for each model and its , it was necessary to factor it by the corresponding Feature Importance Ranks (FIR), see equation (18). Table VIII shows the new recalculated FRC's for the four designed models, and it shows that the RF model still has the lowest cost, which means that the previous post processing analysis still holds.…”

Section: B Feature Selection and Model Designmentioning

confidence: 99%

Cardiovascular Disease Diagnosis: A Machine Learning Interpretation Approach

Meshref¹

2019

IJACSA

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Research on heart diseases has always been the center of attention of the world health organization. More than 17.9 million people died from it in 2016, which represent 31% of the overall deaths globally. Machine learning techniques have been used extensively in that area to assist physicians to develop a firm opinion about the conditions of their heart disease patients. Some of the existing machine learning models still suffers from limited predication ability, and the chosen analysis approaches are not suitable. As well, it was noticed that the existing approaches pay more attention to building high accuracy models, while overlooking the ability to interpret and understand the recommendations of these models. In this research, different renowned machine learning techniques: Artificial Neural Networks, Support Vector Machines, Naïve Bayes, Decision Trees and Random Forests have been investigated to help in building, understanding and interpreting different heart disease diagnosing models. The Artificial Neural Networks model showed the best accuracy of 84.25% compared to the other models. In addition, it was found that despite some designed models have higher accuracies than others, it may be safer to choose a lower accuracy model as a final design of this study. This sacrifice was essential to make sure that a more transparent and trusted model is being used in the heart disease diagnosis process. This transparency validation was conducted using a newly suggested metric: the Feature Ranking Cost index. The use of that index showed promising results by making it clear as which machine learning model has a balance between accuracy and transparency. It is expected that following the detailed analyses and the use of this research findings will be useful to the machine learning community as it could be the basis for post-hoc prediction model interpretation of different clinical data sets.

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Analysis of Neural Networks Based Heart Disease Prediction System

Cited by 53 publications

References 10 publications

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

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

Heart Disease Prediction using Machine Learning Techniques

Cardiovascular Disease Diagnosis: A Machine Learning Interpretation Approach

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