Machine learning algorithm for clustering of heart disease and chemoinformatics datasets

Balaji, K.; Lavanya, K.; Amalanathan, Geetha Mary

doi:10.1016/j.compchemeng.2020.107068

Cited by 15 publications

(4 citation statements)

References 14 publications

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“…Experimental findings show that the suggested approach is more accurate than previous methods. This work suggested employing the UCI machine learning repository's Cleveland heart disease dataset, which consists of only 303 cases, to predict heart illness using machine learning [20]. This study's KNN classifier has an accuracy rate as high as 87%.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Stacked Ensemble Model for Heart Disease Detection and Classification

Abbas,

Sampedro,

Alsubai

et al. 2023

CMC

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Cardiac disease is a chronic condition that impairs the heart's functionality. It includes conditions such as coronary artery disease, heart failure, arrhythmias, and valvular heart disease. These conditions can lead to serious complications and even be life-threatening if not detected and managed in time. Researchers have utilized Machine Learning (ML) and Deep Learning (DL) to identify heart abnormalities swiftly and consistently. Various approaches have been applied to predict and treat heart disease utilizing ML and DL. This paper proposes a Machine and Deep Learning-based Stacked Model (MDLSM) to predict heart disease accurately. ML approaches such as eXtreme Gradient Boosting (XGB), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), and K-Nearest Neighbor (KNN), along with two DL models: Deep Neural Network (DNN) and Fine Tuned Deep Neural Network (FT-DNN) are used to detect heart disease. These models rely on electronic medical data that increases the likelihood of correctly identifying and diagnosing heart disease. Well-known evaluation measures (i.e., accuracy, precision, recall, F1-score, confusion matrix, and area under the Receiver Operating Characteristic (ROC) curve) are employed to check the efficacy of the proposed approach. Results reveal that the MDLSM achieves 94.14% prediction accuracy, which is 8.30% better than the results from the baseline experiments recommending our proposed approach for identifying and diagnosing heart disease.

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Section: Literature Reviewmentioning

confidence: 99%

An Efficient Stacked Ensemble Model for Heart Disease Detection and Classification

Abbas,

Sampedro,

Alsubai

et al. 2023

CMC

View full text Add to dashboard Cite

show abstract

“…Each dataset function can be carried out using the model's attribute relevance property. 23,[42][43][44][45] Furthermore, the feature value of the dataset supplies a score for each feature of the data; the higher the score is, the more significant or applicable it is to the performance variable. Additionally, the function value is the built-in class that comes with tree-based classifiers.…”

Section: Feature Importancementioning

confidence: 99%

Automated detection of coronary artery disease comparing arterial fat accumulation using CNN

Sinha¹,

Sharma

2022

J. Electron. Imag.

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. Congenital heart failure (CHF) due to congestion in the blood is a serious cardiac problem correlated with crippling symptoms and leading to a rising death rate, monumental health care spending, and reduced quality of life. Heart disease prevention is among the most crucial functions of any medical system, as many people are prone to heart attacks worldwide. Although several segmentation methods for great vessels and the heart have been proposed in the research, they are not successful when applied to the health records of congenital heart disease. In this proposed work, the thickness and fat accumulation of most arteries are measured and analyzed, and then the measurement is synthesized with the corresponding width of the blood vessels of arteries; this data is used for training purposes in the convolutional neural network with one-off cross-validation and regularization. Using the CNN model, a confusion matrix is created and different statistical parameters such as accuracy sensitivity, specificity, precision, and f-score are generated. The final average accuracy was 97%, precision was 98.13%, and F-score was 98.36%. The results indicate that the CNN-based strategy can distinguish healthy hearts from those with prior cardiovascular disease.

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“…Various machine learning and deep-learning methods have been developed and trained with biomedical data to have a more accurate decision-making mechanism [6]. Several studies have been reported for the development of heart disease diagnosis frameworks based on machine learning (ML) models with enhanced performance on clinical data parameters [7], [8], [9] and unsupervised learning approaches like discriminatively boosted clustering [10]. Different clinical data parameters such as age, heart rate, blood sugar, cholesterol, and blood pressure were considered to make predictive decisions and XGBoost demonstrated superior performance with an overall accuracy of 95.90% [11].…”

Section: A Related Prior Researchmentioning

confidence: 99%

AI-CardioCare: Artificial Intelligence Based Device for Cardiac Health Monitoring

Joshi

Khan

Pathak

et al. 2022

IEEE Trans. Human-Mach. Syst.

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Cardiac disorders are one of the leading causes of mortality around the globe and early diagnosis of heart diseases can be beneficial for its mitigation. In this article, an artificial intelligence (AI) based device has been proposed, which allows for an automatic and real-time diagnosis of cardiac diseases based on deep learning techniques. The heart sound (phonocardiogram) signal is acquired by a customized designed stethoscope and the signal is processed before analysis using AI methods for the classification of four major cardiac diseases (Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, and Mitral Valve Prolapse). Two deep learningbased neural networks, one-dimensional (1-D) convolutional neural network (CNN) and spectrogram based 2-D-CNN models from the analysis of these signals has been integrated with a low-cost singleboard processor to make a standalone device. All data processing is done in a single hardware setup and user interface is provided allowing the user to control the data accessibility and visibility to generate the diagnostic report. As a result, the developed device has demonstrated to be a valuable low-cost diagnostic tool for both medical professionals and personal usage at home.

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Machine learning algorithm for clustering of heart disease and chemoinformatics datasets

Cited by 15 publications

References 14 publications

An Efficient Stacked Ensemble Model for Heart Disease Detection and Classification

An Efficient Stacked Ensemble Model for Heart Disease Detection and Classification

Automated detection of coronary artery disease comparing arterial fat accumulation using CNN

AI-CardioCare: Artificial Intelligence Based Device for Cardiac Health Monitoring

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