Predicting the chances of occurrence of Cardio Vascular Disease (CVD) in people using classification techniques within fifty years of age

Karthick, D.; Priyadharshini, B.

doi:10.1109/icisc.2018.8398990

Cited by 12 publications

(1 citation statement)

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“…Stroke is caused by a blood clot in an artery within the brain, which cuts off blood circulation to the brain [2]. Heart disease is triggered mostly when the heart is unable to provide enough blood supply to parts of the body [3,4]. It results in early symptoms, such as an irregular heartbeat, shortness of breath, chest discomfort, sudden dizziness, nausea, swollen feet, and a cold sweat.…”

Section: Introductionmentioning

confidence: 99%

Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators

et al. 2021

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Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers from different categories, such as Bayes, functions, lazy, meta, rules, and trees, were trained for efficient heart disease risk prediction using the full set of attributes of the Cleveland heart dataset and the optimal attribute sets obtained from three attribute evaluators. The performance of the algorithms was appraised using a 10-fold cross-validation testing option. Finally, we performed tuning of the hyperparameter number of nearest neighbors, namely, ‘k’ in the instance-based (IBk) classifier. The sequential minimal optimization (SMO) achieved an accuracy of 85.148% using the full set of attributes and 86.468% was the highest accuracy value using the optimal attribute set obtained from the chi-squared attribute evaluator. Meanwhile, the meta classifier bagging with logistic regression (LR) provided the highest ROC area of 0.91 using both the full and optimal attribute sets obtained from the ReliefF attribute evaluator. Overall, the SMO classifier stood as the best prediction method compared to other techniques, and IBk achieved an 8.25% accuracy improvement by tuning the hyperparameter ‘k’ to 9 with the chi-squared attribute set.

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Section: Introductionmentioning

confidence: 99%