Analysis of Demographic Characteristics Creating Coronary Artery Disease Susceptibility Using Random Forests Classifier

Akyol, Kemal; Çalik, Elif; Bayir, Safak; Şen, Baha; Çavuşoğlu, Abdullah

doi:10.1016/j.procs.2015.08.407

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…This paper applies two classification models, namely, Random Forest and XGBoost. The Random Forest algorithm, introduced by Breiman, is a highly effective and most frequently used model, which can be used for classification and regression problems at the same time [29,30]. It belongs to an ensemble learning technology based on bagging.…”

Section: Classification Algorithmmentioning

confidence: 99%

Improvement of the Performance of Models for Predicting Coronary Artery Disease Based on XGBoost Algorithm and Feature Processing Technology

et al. 2022

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Coronary artery disease (CAD) is one of the diseases with the highest morbidity and mortality in the world. In 2019, the number of deaths caused by CAD reached 9.14 million. The detection and treatment of CAD in the early stage is crucial to save lives and improve prognosis. Therefore, the purpose of this research is to develop a machine-learning system that can be used to help diagnose CAD accurately in the early stage. In this paper, two classical ensemble learning algorithms, namely, XGBoost algorithm and Random Forest algorithm, were used as the classification model. In order to improve the classification accuracy and performance of the model, we applied four feature processing techniques to process features respectively. In addition, synthetic minority oversampling technology (SMOTE) and adaptive synthetic (ADASYN) were used to balance the dataset, which included 71.29% CAD samples and 28.71% normal samples. The four feature processing technologies improved the performance of the classification models in terms of classification accuracy, precision, recall, F1 score and specificity. In particular, the XGBboost algorithm achieved the best prediction performance results on the dataset processed by feature construction and the SMOTE method. The best classification accuracy, recall, specificity, precision, F1 score and AUC were 94.7%, 96.1%, 93.2%, 93.4%, 94.6% and 98.0%, respectively. The experimental results prove that the proposed method can accurately and reliably identify CAD patients from suspicious patients in the early stage and can be used by medical staff for auxiliary diagnosis.

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Section: Classification Algorithmmentioning

confidence: 99%

Improvement of the Performance of Models for Predicting Coronary Artery Disease Based on XGBoost Algorithm and Feature Processing Technology

et al. 2022

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“…The machine learning module is supposed to infer a physiological function that relates the features extracted from the PPG signal and the desired target [28]. Literature clearly portrays that most of the work involved in the detection of OSA has used neural network-based classifier [29].…”

Section: Classifier Modulementioning

confidence: 99%

“…RF is a supervised machine learning technique used for classification and regression. It operates by creating decision trees based on the feature parameters during the training process and acquire the prediction from each of the parameter [28]. The precision of the result increases with increase in the number of trees and also avoids over fitting of the model.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

An automatic screening approach for obstructive sleep apnea from photoplethysmograph using machine learning techniques

2021

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Obstructive sleep apnea (OSA), a very common sleep disorder remains as an underdiagnosed root cause for several cardiovascular and cerebrovascular diseases. In this paper, we propose an efficient and accurate system that utilizes a single sensor for effective screening of OSA using machine learning algorithms. The automatic screening system involves a photoplethysmogram (PPG) signal, a novel algorithm to detect and remove the corrupted part of the signal, a feature extraction module to extract several features from the PPG waveform and a classifier module which helps in screening for OSA. The elemental idea behind this work is that there is a characteristic relationship between the shape of the PPG waveform and the oxygen desaturation in the apnea patients. The method as described was tested on 285 subjects, inclusive of both normal and apnea patients, and the results were obtained after 10-fold-cross validation of the different machine learning techniques viz., univariate regression, multivariate regression, support vector machine and random forest. The best results in screening OSA were obtained from random forest algorithm with the highest performance (Acc: 98.0%, Sen: 98.6%, Spec: 99.3%) for all the combined features. The proposed work is an effective system for automatic screening of OSA from a single PPG sensor, thereby reducing the need for a very expensive and overnight polysomnography sleep study.

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“…In 2015, Kemal Akyol and Elif Calik used randomForest classification method and they achieved the ratio of the Correct classification was 97.72% [7]. Parthiban and Subramanian combined neural network data mining algorithm and learning capabilities of fuzzy logic that have a qualitative approach in order to utilize it aimed at diagnosis of heart disease [13].…”

Section: Introductionmentioning

confidence: 99%

A datamining approach for coronary artery disease prediction

Dekamin

Sheibatolhamdi

2017

Journal of Advanced Medical Sciences and Applied Technologies

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Objectives: Coronary artery disease (CAD) is caused by atherosclerosis in coronary arteries and results in cardiac arrest and heart attack. Angiography is one of the most accurate methods to diagnose Heart disease, it incurs high expenses and comes with side effects. Data mining is the extraction of hidden predictive information and unknown data, patterns, relationships and knowledge by exploring the large data sets which are difficult to find and detect with traditional statistical methods. One of the biggest problems that prevent pattern recognition from functioning rapidly and effectively are the noisy and inconsistent data in databases. The present study intends to provide a data preparation method based on clustering algorithms for diagnosis Coronary artery disease with higher efficiency and fewer errors. Materials:In this study, the data under investigation was collected from a number of 303 persons referring to the heart unit in one of Tehran-based hospitals within the time interval 2011 to 2013. It included 54 features. K-means algorithm is used for clustering based data preprocessing system for elimination of noisy and inconsistent data and Naïve Bayes, K nearest neighbor and Decision tree are used for classification. Another two feature subset selection methods for cleaning data are also used to make a comparison between clustering based method and attribute selection method. Rapid Miner Software was adopted to conduct this study.Results: Findings of this research indicated that the suggested model will have the highest efficiency, 90.91. According to the results, the proposed method of performance is highly successful compared to other results attained and seems effective for pattern recognition applications. Conclusion:With these results, the proposed method can be used in the diagnosis of coronary artery disease.

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Analysis of Demographic Characteristics Creating Coronary Artery Disease Susceptibility Using Random Forests Classifier

Cited by 8 publications

References 8 publications

Improvement of the Performance of Models for Predicting Coronary Artery Disease Based on XGBoost Algorithm and Feature Processing Technology

Improvement of the Performance of Models for Predicting Coronary Artery Disease Based on XGBoost Algorithm and Feature Processing Technology

An automatic screening approach for obstructive sleep apnea from photoplethysmograph using machine learning techniques

A datamining approach for coronary artery disease prediction

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