Effective diagnosis of heart disease through neural networks ensembles

By Resul Das, I. Türkoglu, Abdulkadir Sengür. Expert systems with applications, 2009

648 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Health Information Management, Signal Processing

View Paper: https://www.semanticscholar.org/paper/17cd447e313b8405b65be57785fd7c6bea51379e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Qeethara Al-Shayea
2. Manuel Graña
3. Michal Wozniak
4. Sonali Agarwal
5. Amin Ul Haq
6. Divya Tomar
7. Emilio Corchado

In Health Information Management

1. Amna Eleyan
2. Dina Jrab
3. Tarek Bejaoui
4. Derar Eleyan
5. Qeethara Al-Shayea
6. Manuel Graña
7. Gautam Srivastava
8. Michal Wozniak

In Signal Processing

1. Qeethara Al-Shayea
2. Manuel Graña
3. Michal Wozniak
4. Amin Ul Haq
5. Emilio Corchado

Citations in the corpus

• Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning (2021, methodology): “…used is of Cleveland [30]. results obtained achieved great accuracies like random forest with 89.2 percent accuracy [31]; decision tree with 89.1 percent accuracy [32]; ANN with 92.7 percent accuracy [33], 89 percent [33], and 89.7 percent accuracy [34]; and SVM accuracy with 88 percent [34].”
• A survey of multiple classifier systems as hybrid systems (2014, methodology): “[209,210] over features selected by a correlation analysis with the categorical variable.”
• Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques (2021, background): “The authors in [10] analyzed different factors that cause heart disease and identified controllable factors such as alcohol usage, smoking, diabetics, high cholesterol, and limited physical activity.”
• A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms (2018, methodology): “[19] proposed an ANN ensemble-based predictive model that diagnoses the heart disease and used statistical analysis system enterprise miner 5.”
• Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm (2017, background): “In fact, a large number of investigations have been conducted in this field [12-34], with the UCI datasets [35] being the most frequently utilized sets.”
• Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques (2019, result): “The obtained results are compared with the results of existing models within the same domain and found to be improved [10].”
• Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare (2020, methodology): “Furthermore, the performance of the proposed technique have been compared with state of the art existing methods in the literature, such as NB [17], Three phase ANN (Artificial neural Network) diagnosis system [18], Neural network ensembles (NNE) [19], ANN-Fuzzy-AHP diagnosis system (AFP) [20], Adapt”
• Artificial Neural Networks in Medical Diagnosis (2024, methodology): “Das, Turkoglu and Sengur [3] used SAS enterprise miner 5.2 to construct a neural networks ensemble based methodology for diagnosing of the heart disease.”
• A survey on Data Mining approaches for Healthcare (2013, background): “, for diagnosis of heart disease in order to develop effective decision support system [60].”
• Heart Disease Prediction Using Machine Learning Algorithms (2024, methodology): “In 2019, Mohan et al. [16] developed a method for predicting heart disease using hybrid machine-learning techniques.”