A Review of Commonly used Machine Learning Classifiers in Heart Disease Prediction

Tushar, Alimul Mahfuz; Wazed, Abdul; Shawon, Ehsanuzzaman; Rahman, Muntasir Raihan; Hossen, Md. Ismail; Jesmeen, M. Z. H.

doi:10.1109/icspc55597.2022.10001742

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…A model [16] is trained with 1025 Kaggle dataset which is a combination of four different datasets (Cleveland, Hungary, Long beach V and Switzerland) for heart disease prediction. Out of six classifiers Support Vector Machine (SVM), Decision Tree (DT), K-nearest Neighbor (KNN), Naive bayes (NB), Random Forest (RF), DT achieved more accuracy about 98%.…”

Section: Classical Learning (Supervised)mentioning

confidence: 99%

“…Random Forest classifier achieved good accuracy in prediction of heart disease [1,3,23,28,29]. In another study the model gave good accuracy, precision, and recall for Decision Tree [16]. In one of the studies KNN also achieved good accuracy in prediction [20].…”

Section: Performance Measuresmentioning

confidence: 99%

“…With this concern an automated machine learning system can be used to predict the disease in early stages. Machine learning algorithms learn from past clinical data and predict and gives an accurate result to reduce the burden of health practitioner [16]. Machine learning algorithms are complex procedures which identify the patterns and help in decision making by learning from trained datasets.…”

Section: Introductionmentioning

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%