Machine Learning Algorithms for Predicting Coronary Artery Disease: Efforts Toward an Open Source Solution (original) (raw)
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Experiments with Machine Learning in the Prediction of Coronary Artery Disease Progression
Springer eBooks, 1997
Coronary Artery Disease (CAD) is the leading cause of mortality worldwide. It is a complex heart disease that is associated with numerous risk factors and a variety of Symptoms. During the past decade, Coronary Artery Disease (CAD) has undergone a remarkable evolution. The purpose of this research is to build a prototype system using different Machine Learning Algorithms (models) and compare their performance to identify a suitable model. This paper explores three most commonly used Machine Learning Algorithms named as Logistic Regression, Support Vector Machine and Artificial Neural Network. To conduct this research, a clinical dataset has been used. To evaluate the performance, different evaluation methods have been used such as Confusion Matrix, Stratified K-fold Cross Validation, Accuracy, AUC and ROC. To validate the results, the accuracy and AUC scores have been validated using the K-Fold Cross-validation technique. The dataset contains class imbalance, so the SMOTE Algorithm has been used to balance the dataset and the performance analysis has been carried out on both sets of data. The results show that accuracy scores of all the models have been increased while training the balanced dataset. Overall, Artificial Neural Network has the highest accuracy whereas Logistic Regression has the least accurate among the trained Algorithms.
Machine Learning Predictive Models for Coronary Artery Disease
Sn Computer Science, 2021
Coronary artery disease (CAD) is the commonest type of heart disease and over 80% of the deaths resulted from the diseases occurred in developing countries including Nigeria, with majority being in those victims are below 70 years of age. Though, CAD is not a well known disease in Nigeria but however in year 2014, 2.82% of the total of deaths occurred in the country were due to the disease. In this study, a machine leaning predictive models for CAD has been developed with diagnostic CAD dataset obtained in the two General Hospitals in Kano State—Nigeria. The dataset applied on machine learning algorithms which include support vector machine, K nearest neighbor, random tree, Naïve Bayes, gradient boosting and logistic regression algorithms to build the predictive models and the models were evaluated based accuracy, specificity, sensitivity and receiver operating curve (ROC) performance evaluation techniques. In terms of accuracy random forest-based machine learning model emerged to b...
Machine Learning for Diagnosis of Coronary Artery Disease
2019
The main global cause of death is coronary artery disease by the report of the World Health Organization in several years. Furthermore, the medical costs of coronary artery disease are pretty high. The most importantly, heart disease is greatly killed Taiwanese in recent years. Thus, in order to reduce the harm to people, it is necessary to predict coronary artery disease accurately and earlier. The major purpose of this study is to construct different machine learning models on diagnosing of coronary artery disease. The Z-Alizadeh Sani dataset from UCI Machine Learning Repository, including 303 patients and 54 features, was mainly adopted in this study. We apply the 3-fold and 5-fold cross-validation and evaluate the accuracy, sensitivity, specificity, precision, Area Under Curve and Matthews correlation coefficient for different model algorisms, including decision tree, logistic regression and ensemble learning technique. The highly accuracy of 10 features model and 20 features mo...
An Intelligent Machine Learning Approaches for Predicting Coronary Artery Disease
Coronary Artery Disease (CAD) destroys the internal layer of the artery. Consequently, this destruction leads the fatty sediments to escalate the injury. CAD is one of the common significant reasons of death all around the world, thus early detection of CAD will facilitate scale back these rates. The medical industries gather a large number of facts which include some unknown data to make the choice effective. They also use some excellent data processing methods. The CAD prediction indicates the probability of patients getting artery disease. In this research, we propose various Machine Learning (ML) methods to predict the CAD with the help of historical data. These ML methods enable the system to learn over several datasets to acknowledge valuable understanding. The programmable capability of ML in examining, interpreting, and processing data-set is beneficial to decision-makers in the medical field. This method uses 10 medical parameters to forecast artery disease which is obtained from KEEL (Knowledge Extraction based on Evolutionary Learning). An experiment is performed with algorithms like Naive Bayes, Decision Tree, Neural Network (MLP Classifier), Logistic Regression, and Random Forest with necessary performance metrics like accuracy, precision, recall.
Machine Learning Classifications of Coronary Artery Disease
2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), 2018
Coronary Artery Disease (CAD) is one of the leading causes of death worldwide, and so it is very important to correctly diagnose patients with the disease. For medical diagnosis, machine learning is a useful tool; however features and algorithms must be carefully selected to get accurate classification. To this effect, three feature selection methods have been used on 13 input features from the Cleveland dataset with 297 entries, and 7 were selected. The selected features were used to train three different classifiers, which are SVM, Naïve Bayes and KNN using 10-fold cross-validation. The resulting models evaluated using Accuracy, Recall, Specificity and Precision. It is found that the Naïve Bayes classifier performs the best on this dataset and features, outperforming or matching SVM and KNN in all the four evaluation parameters used and achieving an accuracy of 84%.
Machine Learning-Based Classification Algorithms for the Prediction of Coronary Heart Diseases
ArXiv, 2021
Coronary heart disease, which is a form of cardiovascular disease (CVD), is the leading cause of death worldwide. The odds of survival are good if it is found or diagnosed early. The current report discusses a comparative approach to the classification of coronary heart disease datasets using machine learning (ML) algorithms. The current study created and tested several machine-learning-based classification models. The dataset was subjected to Smote to handle unbalanced classes and feature selection technique in order to assess the impact on two distinct performance metrics. The results show that logistic regression produced the highest performance score on the original dataset compared to the other algorithms employed. In conclusion, this study suggests that LR on a well-processed and standardized dataset can predict coronary heart disease with greater accuracy than the other algorithms.
Journal of Healthcare Engineering
Background. In today’s industrialized world, coronary artery disease (CAD) is one of the leading causes of death, and early detection and timely intervention can prevent many of its complications and eliminate or reduce the resulting mortality. Machine learning (ML) methods as one of the cutting-edge technologies can be used as a suitable solution in diagnosing this disease. Methods. In this study, different ML algorithms’ performances were compared for their effectiveness in developing a model for early CAD diagnosis based on clinical examination features. This applied descriptive study was conducted on 303 records and overall 26 features, of which 26 were selected as the target features with the advice of several clinical experts. In order to provide a diagnostic model for CAD, we ran most of the most critical classification algorithms, including Multilayer Perceptron (MLP), Support Vector Machine (SVM), Logistic Regression (LR), J48, Random Forest (RF), K-Nearest Neighborhood (KN...
Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers
Sensors
Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accu...
Metabolites
Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of specific ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and APOB/APOA1 ratio. Patients were grouped into: obstructive CAD (SS > 0) and non-obstructive CAD (SS = 0). A risk prediction algorithm (boosted ensemble algorithm XGBoost) was developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD. The study population comprised 958 patients (CorLipid trial (NCT04580173)), with no prior CAD, who underwent coronary angiography. Of them, 533 (55.6%) suffered ACS, 170 (17.7%) presented with NSTEMI, 222 (23.2%) with STEMI, and 141 (14.7%) with unstable angina. Of the total sample, ...
2020
Background: Chest pain represents one of the most common reasons for consultation in emergency medical services (EMS). A diagnostic strategy using objective and subjective information about the characteristics of chest pain has not been identified yet. Objective: The aim of this study was to evaluate the performance of a machine learning classifier as a tool for prediction of the risk for non-ST-segment elevation acute coronary syndrome (ACS) in patients consulting an EMS due to chest pain. Methods: A total of 161 patients consulting the EMS due to chest pain were analyzed. Both objective and subjective variables about the characteristics of chest pain were recorded using a machine learning classifier. Results: Mean age was 57.43±12 years, 72.7% were men and 17.4% had prior cardiovascular disease. Acute coronary syndrome was present in 57.8% of cases with an incidence of 29.8%. Among the latter 35% required percutaneous coronary intervention and 9.9% myocardial revascularization sur...