A novel scoring system for coronary artery disease risk assessment

Ebenezer, A. Shamila; Rani, P. Arockia Jansi; Narmadha, D.; Sundar, G. Naveen

doi:10.1109/i2c2.2017.8321957

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…They utilized the PIMA dataset and applied various classification techniques such as 'ANN, DT, SVM, RF, CHAID, rule induction, KNN, decision stump (DS) and naive Bayes (NB)'. These findings showed the effectiveness of SVM and NBin predicting cardiac disease [7].…”

Section: Related Workmentioning

confidence: 55%

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Prediction of Cardiovascular Disease using Machine Learning Algorithms

Joshi,

Dembla,

Bhatia

2024

IJACSA

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Heart is the most critical organ of our body for being responsible for regulating and maintaining the blood circulation levels. Globally, heart disease cases are prevalent and constitute a significant cause of mortality. Manifestations such as chest discomfort and irregular heartbeat are notable symptoms. The healthcare sector has amassed substantial knowledge in this domain. Analyzing the research, this paper delves into the concept of utilizing ML algorithms to predict cardiac diseases. In this research will employ a diverse array of machine learning techniques, including decision tree, support vector classifier, random forest, K-NN, logistic regression and naive Bayes. These algorithms utilize specific characteristics to forecast cardiac diseases effectively. Leveraging machine learning algorithms to analyze and predict outcomes from the extensive healthcaregenerated data shows considerable promise. Recent advancements in machine learning models have incorporated numerous features, and in this study, propose the integration of these features in machine learning algorithms to forecast cardiovascular ailments. The main objective of this research is to identify the performance of the mentioned machine learning algorithms for predicting cardiovascular elements.

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Section: Related Workmentioning

confidence: 55%

“…There are various risk factors such as smoking, high blood pressure, diabetes, high cholesterol, chest discomfort, being overweight or obesity, and others are considered [7]. Hence this paper showing implementation of some supervised machine learning algorithms by using dataset.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cardiovascular Disease using Machine Learning Algorithms

Joshi,

Dembla,

Bhatia

2024

IJACSA

View full text Add to dashboard Cite

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“…After preprocessing the dataset and conducting experiments, the results showed that SVM achieved a classification accuracy of 85%, surpassing K-NN and ANN which achieved approximately 82% and 73% respectively. A. S. Ebenezer et al [29] selected ten different algorithms for coronary artery disease risk assessment. They chose artificial neural network (ANN), decision tree (DT), support vector machine (SVM), random forest (RF), CHAID, rule induction, naïve bayes (NB), k-nearest neighbor (KNN), [30] and decision stump (DS) for the classification task.…”

Section: Literature Reviewsmentioning

confidence: 99%

Heart Disease Prediction using SVM

Rahmanul Hoque,

Masum Billah,

Amit Debnath

et al. 2024

Int. J. Sci. Res. Arch.

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Diagnosing and predicting the outcome of cardiovascular disease are essential tasks in medicine that help ensure patients receive accurate classification and treatment from cardiologists. The use of machine learning in the healthcare sector has grown due to its ability to identify patterns in data. By applying machine learning techniques to classify the presence of cardiovascular diseases, it's possible to decrease the rate of misdiagnosis. This study aims to create a model capable of accurately forecasting cardiovascular diseases to minimize the deaths associated with these conditions. In this paper, two types of SVM model such as linear SVM and polynomial SVM is used. Accuracy, precision, recall and F1 score has been evaluated for comparing linear SVM and polynomial SVM. Polynomial SVM provides better accuracy than linear SVM.

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