An intelligent framework for heart disease prediction deep learning-based ensemble Method

Venkatesh, Veeramuthu; Rai, Pethuru; Reddy, Kalluru Amarnath; Praba, S.; Anushiadevi, R.

doi:10.1109/iccpc55978.2022.10072285

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…A framework using deep learning based ensembled method was constructed for heart disease prediction on Cleveland dataset from UCI repository, data preprocessing is done using K-bins discretization, Elliptic envelope, Randomized search CV, Isolation Forest and reduced model component analysis. The framework compares the result with SVC, AdaBoost, MLP, KNN etc., where GB performed well accuracy of 0.98 [36]. An attempt was made using ML and deep learning algorithms for predicting risk of coronary heart disease on Cleveland dataset.…”

Section: Classical Learning (Supervised)mentioning

confidence: 99%

“…In a fewer studies SVM performed well in terms of accuracy [27,28,34]. In the abovementioned models, the combination of LG and grid search performed well [30] and in a few methods deep learning performed well [32,36]. The ensembled methods achieved good accuracy when compared to base classifiers [35] and a combination of machine learning deep learning also gives good accuracy [37] and integrated system also achieved expected accuracy considering k-means clustering [38][39][40][41][42]44].…”

Section: Performance Measuresmentioning

confidence: 99%

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A Review of Machine Learning Techniques Used in the Prediction of Heart Disease

Chaithra,

Siddesha,

Aradhya

et al. 2024

RIA

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Heart disease stands as a principal cause of death worldwide, and its early prediction is essential for effective patient management and the reduction of healthcare expenditures. In this context, machine learning (ML) has emerged as a transformative tool in the healthcare sector, demonstrating a profound capability to discern intricate data patterns and furnish accurate prognostic assessments. The application of ML in cardiology is instrumental for risk prediction, early detection, and the customization of treatment protocols. The current study systematically reviews the spectrum of ML approaches applied to the prediction of heart disease, spanning supervised, unsupervised, reinforcement, and transfer learning methodologies. Data from prominent repositories such as Kaggle and the UCI Machine Learning Repository were employed to evaluate the performance of various ML algorithms, with key metrics including accuracy, sensitivity, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Influential predictors, namely age, gender, cholesterol levels, blood pressure, and lifestyle factors, were integral to the development of these predictive models. Particular attention was given to the exploration of ensemble methods and deep learning frameworks, which have shown to augment prediction accuracy beyond that of traditional models. This research delineates essential risk factors associated with heart disease and underscores the significance of predictive analytics in the healthcare landscape. With a focus on heterogeneous datasets and analytical techniques, the review aims to inform public health strategies and contribute to the alleviation of healthcare burdens. The elucidated findings highlight the promise of ML, particularly through the utilization of ensemble and deep learning methods, in the precursory prediction of heart disease. Such advancements enable healthcare professionals to make more informed decisions, adopt preventative interventions, and mitigate the overall impact on healthcare systems. This exhaustive review also synthesizes the efficacy and practicality of various ML algorithms, providing a valuable compendium for future research initiatives and promoting the integration of cutting-edge technologies in the management of cardiac health.

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Section: Classical Learning (Supervised)mentioning

confidence: 99%