Decision Tree Algorithms for Prediction of Heart Disease

Maji, Srabanti; Arora, Srishti

doi:10.1007/978-981-13-0586-3_45

Cited by 92 publications

(34 citation statements)

References 5 publications

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“…Decision tree (DT), utilize to represent the probability relationship between feature. In the Fig.8 DT, the result with respect to accuracy includes scenario-1 to scenario-6 [18].…”

Section: ) Decision Treementioning

confidence: 99%

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Notice of Retraction: Molecular Diagnostic and Using Deep Learning Techniques for Predict Functional Recovery of Patients Treated of Cardiovascular Disease

et al. 2019

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Today, with the development of industry and mechanized life style, the prevalence of the disease is rising steadily as well. Observing at the trend and lifecycle style, its predict that after ten years around 23.6 million people die because of Cardiovascular Disease (CVD). For that reason, aim to use Deep Learning Techniques (DLTs), to analysis stable CVD that would give valuable awareness to decrease misdiagnosis in the Robust Healthcare Industry (RHI). An objective of this paper is first, Molecular diagnosis (MD), and second using Deep Learning Techniques DLTs, to synthesis and characterize to accumulate (raw information) from CVD patients, those who admitted the emergency section between January (2018 to December 2019). We are using Artificial Neural Network (ANN), model characterize to predict CVD patients and configuration, Feature selection (FS), Mean Square Error (MSE), accuracy, sensitivity. The ANN accuracy is 98.4, K-nearest neighbor (KNN) accuracy is 98.01%, Naïve Bayes (NB), accuracy is 96.99%. Decision tree (DT), accuracy is 87.81%. Our robust data driven model explore the efficient accuracy rate to predict CVD patients. The ANN model in term of their efficient in disease analysis, and prognosis of the RHI. INDEX TERMS Artificial neural network (ANN), cardiovascular disease (CVD), molecular diagnostic (MD), Robust Healthcare Industry (RHI).

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“…Decision tree (DT), utilize to represent the probability relationship between feature. In the Fig.8 DT, the result with respect to accuracy includes scenario-1 to scenario-6 [18].…”

Section: ) Decision Treementioning

confidence: 99%

“…wherever x, is the input vector, z, the output, scalar bias is the b, and w is the weight matrix [18].…”

Section: ) Artificial Neural Network Ann Model Construction and Evamentioning

confidence: 99%

Notice of Retraction: Molecular Diagnostic and Using Deep Learning Techniques for Predict Functional Recovery of Patients Treated of Cardiovascular Disease

et al. 2019

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“…A confidence measure-based (CMB) technique was applied to assess the test set, and they attained 77.5% accuracy. Maji et al [28] proposed a hybridization technique for a DT and an artificial neural network (ANN) and obtained 78.14% accuracy and 77.4% accuracy, respectively. 10-fold cross-validation was used to substantiate their results, and the experiments were carried out in WEKA.…”

Section: B Heart Disease Prediction Techniquesmentioning

confidence: 99%

Novel Feature Reduction (NFR) Model With Machine Learning and Data Mining Algorithms for Effective Disease Risk Prediction

Pasha

Mohamed

2020

IEEE Access

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Presently, the application of machine learning (ML) and data mining (DM) techniques have a vital role in healthcare systems and wisely convert all obtainable data into beneficial knowledge. It is proven from the literature works that a chance of 12% error remains in the diagnosis of the diseases by the medical practitioners. Moreover, for effective disease risk prediction in medical analysis, more emphasis is accorded to the area under the curve (AUC) with accuracy as an evaluation metric. However, the role of the AUC has not been previously characterized notably. In this research article, a novel feature reduction (NFR) model that is aligned with the ML and DM algorithms is proposed to reduce the error rate and further improve the performance. The proposed NFR model comprises of two approaches and uses the AUC in addition to the accuracy to achieve a robust and effective disease risk prediction. The first approach is based on a heuristic process evaluating performance by reducing features with respect to the improvement in the AUC besides the accuracy as evaluation metrics, working to obtain the best subset of highly contributing features in the prediction. The second approach evaluates the accuracy and AUC of all individual features and forms the subsets with the highest accuracies, AUCs, and least difference between them, which are combined in various combinations to achieve the best-reduced set of highly relevant features. For this purpose, the benchmarked public heart datasets of the ML repository of the University of California, Irvine (UCI) are tested; the results are promising. The highest accuracy and AUC achieved with the proposed NFR model are 95.52% and 99.20% with 41.67% feature reduction, respectively. The accuracy is 4.22% higher than recent existing research with a significant improvement of 25% in the performance of the running time of the algorithm.

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“…Therefore, recent studies have begun to use the hybrid structure of the two algorithms (Pu et. al., 2019;Maji & Arora, 2019).…”

Section: Data Mining and Machine Learningmentioning

confidence: 99%

A research on the comparison of classification algorithm in finance

Etı

İnel

2019

CyA

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Nowadays, data is developing with technology. With developments in the Internet and technology, it makes a warehouse that changes the data more rapidly and in volume. This situation increases the importance of data mining every day. The aim of this study is to examine classification algorithms which are the sub-subject of data mining. In the analysis part of the study, it is aimed to compare the algorithms with the accuracy ratio by using the financial data. As a result of the study conducted on stock marketing, the classification of enterprises of decision tree models with artificial neural networks has also been found to be more successful than other methods.

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Decision Tree Algorithms for Prediction of Heart Disease

Cited by 92 publications

References 5 publications

Notice of Retraction: Molecular Diagnostic and Using Deep Learning Techniques for Predict Functional Recovery of Patients Treated of Cardiovascular Disease

Notice of Retraction: Molecular Diagnostic and Using Deep Learning Techniques for Predict Functional Recovery of Patients Treated of Cardiovascular Disease

Novel Feature Reduction (NFR) Model With Machine Learning and Data Mining Algorithms for Effective Disease Risk Prediction

A research on the comparison of classification algorithm in finance

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