A Knowledge-Based Clinical Decision Support System Utilizing an Intelligent Ensemble Voting Scheme for Improved Cardiovascular Disease Prediction

Bashir, Saba; Almazroi, Abdulwahab Ali; Ashfaq, Sufyan; Almazroi, Abdulaleem Ali; Khan, Farhan Hassan

doi:10.1109/access.2021.3110604

Cited by 24 publications

(10 citation statements)

References 30 publications

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“…Further, NB optimized with grid search found to provide accuracy of 84.8%. Instead of conventional model, an ensemble based majority voting scheme is proposed by researchers [18]. Outliers in the dataset are identified and removed using filter based techniques.…”

Section: Comparison and Discussionmentioning

confidence: 99%

An Effective Heart Disease Prediction Framework based on Ensemble Techniques in Machine Learning

Yewale¹,

Vijayaragavan²,

Bairagi³

2023

IJACSA

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To design a framework for effective prediction of heart disease based on ensemble techniques, without the need of feature selection, incorporating data balancing, outlier detection and removal techniques, with results that are still at par with cutting-edge research. In this study, the Cleveland dataset, which has 303 occurrences, is used from the UCI repository. The dataset comprises 76 raw attributes, however only 14 of them are listed by the UCI repository as significant risk factors for heart disease when the dataset is uploaded as an open source dataset. Data balancing strategies, such as random over sampling, are used to address the issue of unbalanced data. Additionally, an isolation forest is used to find outliers in multivariate data, which has not been explored in previous research. After eliminating anomalies from the data, ensemble techniques such as bagging, boosting, voting, stacking are employed to create the prediction model. The potential of the proposed model is assessed for accuracy, sensitivity, and specificity, positive prediction value (PPV), negative prediction value (NPV), F1 score, ROC-AUC and model training time. For the Cleveland dataset, the performance of the suggested methodology is superior, with 98.73% accuracy, 98% sensitivity, 100% specificity, 100% PPV, 97% NPV, 1 as F score, and AUC as 1 with comparatively very less training time. The results of this study demonstrate that our proposed approach significantly outperforms the existing scholarly work in terms of accuracy and all the stated performance metrics. No earlier research has focused on these many performance parameters.

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Section: Comparison and Discussionmentioning

confidence: 99%

An Effective Heart Disease Prediction Framework based on Ensemble Techniques in Machine Learning

Yewale¹,

Vijayaragavan²,

Bairagi³

2023

IJACSA

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“…Additionally, a novel voting strategy based on ensemble learning was proposed in [25], which made use of six ML algorithms: NB, SVM, DT, neural network (NN), MLP, and single-layer perceptron. The researchers found that, on average, the ensemble model achieved 83% accuracy, which was higher than any of the individual methods.…”

Section: Related Workmentioning

confidence: 99%

An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection

Korial,

Gorial,

Humaidi

2024

Computers

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Cardiovascular disease (CVD) is a leading cause of death globally; therefore, early detection of CVD is crucial. Many intelligent technologies, including deep learning and machine learning (ML), are being integrated into healthcare systems for disease prediction. This paper uses a voting ensemble ML with chi-square feature selection to detect CVD early. Our approach involved applying multiple ML classifiers, including naïve Bayes, random forest, logistic regression (LR), and k-nearest neighbor. These classifiers were evaluated through metrics including accuracy, specificity, sensitivity, F1-score, confusion matrix, and area under the curve (AUC). We created an ensemble model by combining predictions from the different ML classifiers through a voting mechanism, whose performance was then measured against individual classifiers. Furthermore, we applied chi-square feature selection method to the 303 records across 13 clinical features in the Cleveland cardiac disease dataset to identify the 5 most important features. This approach improved the overall accuracy of our ensemble model and reduced the computational load considerably by more than 50%. Demonstrating superior effectiveness, our voting ensemble model achieved a remarkable accuracy of 92.11%, representing an average improvement of 2.95% over the single highest classifier (LR). These results indicate the ensemble method as a viable and practical approach to improve the accuracy of CVD prediction.

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“…Heart disease is considered a chronic disease that can be detected earlier by measuring various health standards, e.g., blood pressure, cholesterol, heart rate, and glucose level [2]. Heart disease does not affect human health alone; it affects the capabilities of countries and their economies [3]. That is, heart disease is a serious disease in which the rate of occurrence is very high, especially in developing countries, due to the lack of knowledge of its symptoms [4].…”

Section: Introductionmentioning

confidence: 99%

Classification and Diagnosis of Heart Disease Using Machine Learning

Mohsen‬‏,

Naoufel,

Alrashahy

et al. 2024

Preprint

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Heart disease is one of the most common causes of death worldwide, and accurate and rapid diagnosis is needed to avoid serious complications. This study aimed to present a system for diagnosing and classifying common heart diseases using machine learning techniques. The data used are a set of medical records for heart patients at Ibb Medical Clinic, Ibb City, Yemen. Fourteen different classification algorithms were implemented using the Weka tool to classify different heart diseases, including eight heart diseases. The results showed that the RF algorithm was the best in terms of accuracy, sensitivity, specification, and F-measure. Based on this algorithm, a web system based on the ASPX language was designed to help users enter patient data and obtain a diagnosis and classification of heart disease. The system can help doctors in the diagnosis process and increase the medical awareness of the community.

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A Knowledge-Based Clinical Decision Support System Utilizing an Intelligent Ensemble Voting Scheme for Improved Cardiovascular Disease Prediction

Cited by 24 publications

References 30 publications

An Effective Heart Disease Prediction Framework based on Ensemble Techniques in Machine Learning

An Effective Heart Disease Prediction Framework based on Ensemble Techniques in Machine Learning

An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection

Classification and Diagnosis of Heart Disease Using Machine Learning

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