Identification of significant features and data mining techniques in predicting heart disease

Amin, Mohammad Shafenoor; Chiam, Yin Kia; Varathan, Kasturi Dewi

doi:10.1016/j.tele.2018.11.007

Cited by 389 publications

(168 citation statements)

References 8 publications

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“…Chiam, and K.D. Varathan [13] reviewed the existing methodologies for a significant feature selection and data mining techniques for heart disease prediction. The hybrid features were used to develop the predictive model and tested with seven classification methods including SVM, and Naïve Bayes.…”

Section: Literature Surveymentioning

confidence: 99%

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A Prediction Technique for Heart Disease Based on Long Short Term Memory Recurrent Neural Network

Manur¹,

Christ²,

Pani³

et al. 2020

IJIES

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In recent years, heart disease is one of the leading cause of death for both women and men. So, heart disease prediction is considered as a significant part in the clinical data analysis. Standard data mining techniques like Support Vector Machine (SVM), Naïve Bayes and other machine learning techniques used in the earlier research for heart disease prediction. These methods are not sufficient for effective heart disease prediction due to insufficient test data. In this research, Bi-directional Long Short Term Memory with Conditional Random Field (BiLSTM-CRF) has been proposed to increase the efficiency of heart disease prediction. The input medical data were analyzed in a bidirectional manner for effective analysis, and CRF provided the linear relationship between the features. The BiLSTM-CRF method has been tested on the Cleveland dataset to analyze the performance and compared with existing methods. The results showed that the proposed BiLSTM-CRF outperformed the existing methods in heart disease prediction. The average accuracy of the proposed BiLSTM-CRF is 90.04%, which is higher than the existing methods.

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Section: Literature Surveymentioning

confidence: 99%

“…The proposed method (BiLSTM-CRF) tested on Cleveland dataset in light of precision, f-measure, and accuracy. In addition, the BiLSTM-CRF method is compared with the standard data mining techniques [13][14][15] in heart disease prediction for evaluating the effectiveness of the proposed work.…”

Section: Performance Analysismentioning

confidence: 99%

A Prediction Technique for Heart Disease Based on Long Short Term Memory Recurrent Neural Network

Manur¹,

Christ²,

Pani³

et al. 2020

IJIES

View full text Add to dashboard Cite

show abstract

“…Today, we face a huge amount of data in industry as well as organizations such as the Healthcare [1,4]. Since data collection and analysis are difficult, time consuming and costly, we are always looking for a way to optimum use of data to achieve the correct decision that can be referred to diagnose and experiment of diseases in healthcare organizations [3].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, in this paper, has been paid attention on heart disease case study in order to apply a prediction method on coronary artery disease [6,12]. One way to accurately diagnose this disease is to use data mining methods to build an appropriate and robust model that is more reliable than medical imaging tools, including angiography in the field of diagnosis of coronary heart disease [4][5][6]. A main challenge in model learning is the feature selection problem so that the feature selection step is so important in data mining and its purpose is to eliminate unnecessary and unimportant features [1,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model

Joloudari

Saadatfar

et al. 2020

IJERPH

124

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Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, the coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that RTs model outperforms other models.

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“…Several studies use data mining and artificial intelligence techniques for diagnosis and detection [7][8][9][10][11][12][13]. In 1995, data mining and machine learning were used in decision-making to detect breast cancer [14].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System

Mojrian

Pintér

Joloudari

et al. 2020

Preprint

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Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R 2 , MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model. Keywords-hybrid machine learning, extreme learning machine (ELM), radial basis function (RBF), breast cancer, support vector machine (SVM),

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Identification of significant features and data mining techniques in predicting heart disease

Cited by 389 publications

References 8 publications

A Prediction Technique for Heart Disease Based on Long Short Term Memory Recurrent Neural Network

A Prediction Technique for Heart Disease Based on Long Short Term Memory Recurrent Neural Network

Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model

Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System

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