Coronary Artery Disease Diagnosis: Ranking the Significant Features Using Random Trees Model

Joloudari, Javad Hassannataj; Joloudari, Edris Hassannataj; Saadatfar, Hamid; GhasemiGol, Mohammad; Razavi, Seyyed Mohammad; Mosavi, Amir; Nabipour, Narjes; Shamshirband, Shahaboddin; Nádai, László

doi:10.20944/preprints202001.0220.v1

Cited by 24 publications

(9 citation statements)

References 6 publications

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“…Machine learning is increasingly common in big data science, with rapid uptake for medical applications 13 – 16 . There are advantages in using machine learning in risk predictions based on a wide array of patient data 17 , 18 . These can be used as decision support tools to aid prescribing of drugs in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

Lü

Bennamoun

Ward

et al. 2021

Sci Rep

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Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction.

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Section: Introductionmentioning

confidence: 99%

Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

Lü

Bennamoun

Ward

et al. 2021

Sci Rep

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show abstract

“…There are numerous works conducted on heart failure detection with machine learning and medical data analytics [13][14][15][16][17][18][19] although, heart failure detection remained a challenging task using predictive model. Some of the recent achievements and works in medical data analysis and heart failure identification with the help of predictive model are discussed in this section.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Overall, predictive accuracy of 81.9% is achieved using KNN algorithm. In another study [14], the authors employed random forest algorithm for designing and implementing model for heart attack classification. The performance of the proposed random forest based heart disease prediction model is validated on the test set.…”

Section: Literature Reviewmentioning

confidence: 99%

Correlation Analysis for Determining Effective Data in Machine Learning: Detection of Heart Failure

Assegie

Sushma

Bhavya³

et al. 2021

SN COMPUT. SCI.

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Heart disease is one of the causes for death throughout the world. Heart disease cannot be easily identified by the medical experts and practitioners as the detection of heart disease requires expertise and experience. Hence, developing better performing models for heart disease detection using machine-learning algorithms is crucial for detecting heart disease in an early stage. However, employing machine learning algorithm involves determining the relationship between the heart failure dataset features. In this study, correlation analysis is employed to identify the relationship among the heart failure dataset features and a predictive model for heart failure detection is developed with K-nearest neighbor (KNN). Pearson correlation is employed to identify the relationship between the features in the heart failure dataset and the effect of strong correlation to the target feature on the performance of K-nearest neighbor (KNN) model is analyzed. The experimental result shows that highly correlated feature significantly affected the performance of K-nearest neighbor (KNN) for heart failure detection. Finally, the performance of KNN is evaluated and result reveals that the model has acceptable level of performance with highest accuracy of 97.07% on heart failure prediction.

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“…Generally, any machine learning method applies the following steps; data preparation, algorithm selection, training, regularization, and evaluation (18). Different methods of machine learning models for coronary artery disease were previously built and analyzed (19)(20)(21). Nevertheless, the circumstances may vary based on different situations, lifestyles, and accessible data and features.…”

Section: Introductionmentioning

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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Coronary Artery Disease Diagnosis: Ranking the Significant Features Using Random Trees Model

Cited by 24 publications

References 6 publications

Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

Correlation Analysis for Determining Effective Data in Machine Learning: Detection of Heart Failure

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

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