Coupling a Fast Fourier Transformation With a Machine Learning Ensemble Model to Support Recommendations for Heart Disease Patients in a Telehealth Environment

Zhang, Ji; Lafta, Raid; Tao, Xiaohui; Li, Yan; Chen, Fulong; Luo, Yonglong; Zhu, Xiaodong

doi:10.1109/access.2017.2706318

Cited by 48 publications

(18 citation statements)

References 38 publications

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“…Many existing studies have presented different approaches, including different machine learning and deep learning techniques for prediction in general [19], [20] and particularly to diagnose and predict heart disease correctly [6], [7]. Many techniques including support vector machine (SVM), artificial neural network (ANN), fuzzy logic, deep neural networks (DNN), decision trees, and long short-term memory have been applied for identifying heart disease symptoms in patients.…”

Section: Related Workmentioning

confidence: 99%

An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

et al. 2020

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A rapid increase in heart disease has occurred in recent years, which might be the result of unhealthy food, mental stress, genetic issues, and a sedentary lifestyle. There are many advanced automated diagnosis systems for heart disease prediction proposed in recent studies, but most of them focus only on feature preprocessing, some focus on feature selection, and some only on improving the predictive accuracy. In this study, we focus on every aspect that may have an influence on the final performance of the system, i.e., to avoid overfitting and underfitting problems or to solve network configuration issues and optimization problems. We introduce an optimally configured and improved deep belief network named OCI-DBN to solve these problems and improve the performance of the system. We used the Ruzzo-Tompa approach to remove those features that are not contributing enough to improve system performance. To find an optimal network configuration, we proposed a stacked genetic algorithm that stacks two genetic algorithms to give an optimally configured DBN. An analysis of a RBM and DBN trained is performed to give an insight how the system works. Six metrics were used to evaluate the proposed method, including accuracy, sensitivity, specificity, precision, F1 score, and Matthew's correlation coefficient. The experimental results are compared with other state-of-the-art methods, and OCI-DBN shows a better performance. The validation results assure that the proposed method can provide reliable recommendations to heart disease patients by improving the accuracy of heart disease predictions by up to 94.61%.

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Section: Related Workmentioning

confidence: 99%

An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

et al. 2020

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“…Data mining is used to analyze a number of datasets and extracts data with classification techniques. It is used to predict the patterns and trends for decision making (Zhang et al, 2017;Amin et al, 2019). The major aim of this research work is to predict whether diabetic patients will get chance to heart disease or not.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

Channabasavaraju¹,

Vinayakamurthy²

2020

Journal of Computer Science

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Data Mining Techniques are used in many areas like banking, healthcare, education because of extracting relevant information from the database. Many algorithms in data mining are used to predict the disease to reduce the patient treatment cost as well as increase the diagnosis accuracy. In this research work, the risk level of diabetic heart disease patient can be predicted using two datasets such as Pima Indians Diabetes dataset and heart disease dataset. Based on the World Health Organization (WHO), diabetes is the one of the biggest health concerns so mining the diabetes data is an ambiguous task. Diabetic patients may also have to suffer from other diseases like heart disease, eye complications, kidney disease, nerve damage, foot problems, skin complications and dental diseases. However, the existing techniques faced the difficulty to identify inconsistent and redundant features. In this research work, the Random Forest Feature Selection Technique (RFS) is developed to identify the significant features and eliminate irrelevant features which are used to improve the predicting accuracy of Cardio Vascular (CV) disease for diabetic patients. According to the output from the extensive experiments, this research categorizes that whether the diabetic patients having CV or not by using True Positive as diabetes and True Negative as no diabetes.

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“…Many researchers have investigated various machine learning methods: LR, KNN, ANN, DT, NB, Support Vector Machine, Fuzzy Inference System, Linear Discriminant Analysis, Rough set [4,12,19]. Some studies have evaluated ensemble or hybrid model for predictions [15,16]. Whereas few researchers have worked on clustering methods.…”

Section: Introductionmentioning

confidence: 99%

Heart Disease Prediction using Ensemble Methods

Deshmukh*¹

2019

IJRTE

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Nowadays, people are suffering from many health issues. One of them is heart disease among the worldwide population. This causes due to imbalance lifestyle and unhealthy food consumption. The data generated by hospitals is huge and complex by nature which store patients medical and demographic information. Accurate and prompt diagnosis of heart diseases are becoming more challenging task in medical domain due to the complex data. Therefore, the computer aided systems are useful to store this complex and multivariate data to generate useful decisions. Machine learning techniques are used to classify and to predict the diseases. In this study, Majority voting classifier and Bagging ensemble method both have been evaluated. These ensemble methods combined the five base classifiers including DT (Decision Tree), LR (Logistic Regression), ANN (Artificial Neural Network), NB (Naïve Bayesian), and KNN (K-Nearest Neighbour). Bagging ensemble approach is used to combine the multiple classifiers prediction abilities for better performance. Experimental work is performed on Cleveland dataset using 14 attributes which is available online on UCI Repository. The results showed that the Bagging ensemble method is performed better to achieve higher accuracy of 87.78 %.

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Coupling a Fast Fourier Transformation With a Machine Learning Ensemble Model to Support Recommendations for Heart Disease Patients in a Telehealth Environment

Cited by 48 publications

References 38 publications

An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

Heart Disease Prediction using Ensemble Methods

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