Predicting coronary artery disease: a comparison between two data mining algorithms

Ayatollahi, Haleh; Gholamhosseini, Leila; Salehi, Masoud

doi:10.1186/s12889-019-6721-5

Cited by 73 publications

(32 citation statements)

References 53 publications

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“…Because of the increased CAD prevalence and consequently the heavy nancial pressure on the society, nding ways to effectively predict this disease is a major desire of healthcare communities (33). Data mining might be used to notify individualized preventive actions and also de ne the impacts of each variables on the studied association.…”

Section: Discussionmentioning

confidence: 99%

Dietary protein is the strong predictor of coronary artery disease; a data mining approach

Soflaei

Shamsara

Sahranavard

et al. 2021

Clinical Nutrition ESPEN

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Backgrounds and aims: Coronary artery disease (CAD) is the major cause of mortality and morbidity globally. Diet is known to contribute to CAD risk, and the dietary intake of speci c macro-or micronutrients might be potential predictors of CAD risk. Machine learning methods may be helpful in the analysis of the contribution of several parameters in dietary including macro-and micro-nutrients to CAD risk. Here we aimed to determine the most important dietary factors for predicting CAD. Methods: Total 273 cases with more than 50% obstruction in at least one coronary artery and 443 healthy controls who completed a food frequency questionnaire (FFQ) were entered into the study. All dietary intakes were adjusted for energy intake. QUEST method was applied to determine the diagnosis pattern of CAD. Results: Total 34 dietary variables obtained from FFQ were entered the study that 23 of these variables were signi cantly associated with CAD according to t-test. Out of 23 dietary input variables adjusted protein, manganese, biotin, zinc and cholesterol remained in the model. According to our tree, only protein intake could identify the patients with coronary artery stenosis according to angiography from healthy participant up to 80%. Manganese dietary intake was the second important variable after protein. The accuracy of the tree was 84.36% for training dataset and 82.94% for testing dataset. Conclusion: Among different macro-and micro-nutrients in the dietary, a combination of protein, manganese, biotin, zinc and cholesterol could predict the presence of CAD.

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Section: Discussionmentioning

confidence: 99%

Dietary protein is the strong predictor of coronary artery disease; a data mining approach

Soflaei

Shamsara

Sahranavard

et al. 2021

Clinical Nutrition ESPEN

View full text Add to dashboard Cite

show abstract

“…Because of the increased CAD prevalence and consequently the heavy nancial pressure on the society, nding ways to effectively predict this disease is a major desire of healthcare communities (34). Data mining might be used to notify individualized preventive actions and also de ne the impacts of each variables on the studied association.…”

Section: Discussionmentioning

confidence: 99%

Dietary protein is the strong predictor of coronary artery disease; a data mining approach

Soflaei

Shamsara

Sahranavard

et al. 2020

Preprint

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Backgrounds: Coronary artery disease (CAD) is the major cause of mortality and morbidity globally. Diet is known to contribute to CAD risk, and the dietary intake of specific macro- or micro-nutrients might be potential predictors of CAD risk. Machine learning methods may be helpful in the analysis of the contribution of several parameters in dietary including macro- and micro-nutrients to CAD risk. Here we aimed to determine the most important dietary factors for predicting CAD.Methods: A total of 273 cases with more than 50% obstruction in at least one coronary artery and 443 healthy controls who completed a food frequency questionnaire (FFQ) were entered into the study. All dietary intakes were adjusted for energy intake. The QUEST method was applied to determine the diagnosis pattern of CAD.Results: A total of 34 dietary variables obtained from the FFQ were entered into the initial study analysis, of these variables 23 were significantly associated with CAD according to t-tests. Of these 23 dietary input variables, adjusted protein, manganese, biotin, zinc and cholesterol remained in the model. According to our tree, only protein intake could identify the patients with coronary artery stenosis according to angiography from healthy participant up to 80%. The dietary intake of manganese was the second most important variable. The accuracy of the tree was 84.36% for the training dataset and 82.94% for the testing dataset.Conclusion: Among several dietary macro- and micro-nutrients , a combination of protein, manganese, biotin, zinc and cholesterol could predict the presence of CAD in individuals undergoing angiography.

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“…Reference [5] performed a comparative study between ANN and SVM classification algorithms based on Positive Predictive value (PPV) of cardiovascular diseases. Their data was obtained from three selected hospitals affiliated to AJA University of Medical Sciences, Iran.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques

Yahaya¹,

Oye²,

Garba³

2020

AJAI

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Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart disease diagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostic tools and shortage of medical professionals and other resources which affect proper prediction and treatment of patients. Inadequate preventive measures, lack of experienced or unskilled medical professionals in the field are the leading contributing factors. Although, large proportion of heart diseases is preventable but they continue to rise mainly because preventive measures are inadequate. In today's digital world, several clinical decision support systems on heart disease prediction have been developed by different scholars to simplify and ensure efficient diagnosis. This paper investigates the state of the art of various clinical decision support systems for heart disease prediction, proposed by various researchers using data mining and machine learning techniques. Classification algorithms such as the Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Network (ANN) have been widely employed to predict heart diseases, where various accuracies were obtained. Hence, only a marginal success is achieved in the creation of such predictive models for heart disease patients therefore, there is need for more complex models that incorporate multiple geographically diverse data sources to increase the accuracy of predicting the early onset of the disease.

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Predicting coronary artery disease: a comparison between two data mining algorithms

Cited by 73 publications

References 53 publications

Dietary protein is the strong predictor of coronary artery disease; a data mining approach

Dietary protein is the strong predictor of coronary artery disease; a data mining approach

Dietary protein is the strong predictor of coronary artery disease; a data mining approach

A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques

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