Machine learning algorithm-based risk prediction model of coronary artery disease

2018 ◽  
Vol 45 (5) ◽  
pp. 901-910 ◽  
Author(s):  
Shaik Mohammad Naushad ◽  
Tajamul Hussain ◽  
Bobbala Indumathi ◽  
Khatoon Samreen ◽  
Salman A. Alrokayan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Prediction Model ◽  
Risk Prediction ◽  
Learning Algorithm ◽  
Risk Prediction Model ◽  
Machine Learning Algorithm ◽  
Artery Disease

scholarly journals A New Model for Caries Risk Prediction in Teenagers Using a Machine Learning Algorithm Based on Environmental and Genetic Factors

2021 ◽  
Vol 12 ◽  
Author(s):  
Liangyue Pang ◽  
Ketian Wang ◽  
Ye Tao ◽  
Qinghui Zhi ◽  
Jianming Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Factors ◽  
Learning Algorithm ◽  
Risk Prediction Model ◽  
Machine Learning Algorithm ◽  
Caries Risk ◽  
New Model ◽  
High Caries Risk

Dental caries is a multifactorial disease that can be caused by interactions between genetic and environmental risk factors. Despite the availability of caries risk assessment tools, caries risk prediction models incorporating new factors, such as human genetic markers, have not yet been reported. The aim of this study was to construct a new model for caries risk prediction in teenagers, based on environmental and genetic factors, using a machine learning algorithm. We performed a prospective longitudinal study of 1,055 teenagers (710 teenagers for cohort 1 and 345 teenagers for cohort 2) aged 13 years, of whom 953 (633 teenagers for cohort 1 and 320 teenagers for cohort 2) were followed for 21 months. All participants completed an oral health questionnaire, an oral examination, biological (salivary and cariostate) tests, and single nucleotide polymorphism sequencing analysis. We constructed a caries risk prediction model based on these data using a random forest with an AUC of 0.78 in cohort 1 (training cohort). We further verified the discrimination and calibration abilities of this caries risk prediction model using cohort 2. The AUC of the caries risk prediction model in cohort 2 (testing cohort) was 0.73, indicating high discrimination ability. Risk stratification revealed that our caries risk prediction model could accurately identify individuals at high and very high caries risk but underestimated risks for individuals at low and very low caries risk. Thus, our caries risk prediction model has the potential for use as a powerful community-level tool to identify individuals at high caries risk.

Outcomes and Risk Prediction Model for Peripheral Arterial Disease in Patients with Stable Coronary Artery Disease

Angiology ◽  
2011 ◽  
Vol 62 (6) ◽  
pp. 473-479 ◽  
Author(s):  
Apurva O Badheka ◽  
Ankit D Rathod ◽  
Aditya S Bharadwaj ◽  
Samrat Bhat ◽  
Mohammad A Kizilbash ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Peripheral Arterial Disease ◽  
Prediction Model ◽  
Risk Prediction ◽  
Stable Coronary Artery Disease ◽  
Arterial Disease ◽  
Risk Prediction Model ◽  
Artery Disease ◽  
Peripheral Arterial

P6435A diagnostic prediction model of coronary artery disease in patient with chest pain using machine learning

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S W Rha ◽  
B G Choi ◽  
S Y Choi ◽  
J K Byun ◽  
J A Cha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Chest Pain ◽  
Coronary Artery ◽  
Prediction Model ◽  
Roc Analysis ◽  
Learning Model ◽  
Coronary Syndrome ◽  
Artery Disease ◽  
Registry Database

Abstract Background Chest pain is a major symptom of coronary artery disease (CAD), which can lead to acute coronary syndrome and sudden cardiac death. Accurate diagnosis of CAD in patients who experience chest pain is crucial to provide appropriate treatment and optimize clinical outcomes. Objective This study was to develop a machine learning model which can predict and diagnose CAD in patients complaining of chest pain based on a large real-world prospective registry database and computing power. Method A total of 10,177 subjects with typical or atypical chest pain who underwent a coronary angiography at the cardiovascular center of our University Hospital, South Korea between November 2004 and May 2014 were evaluated in this study. The generation of the diagnostic prediction model for CAD used the classification application by technical support of MATLAB R2017a. The performance evaluation of the learning model generated by machine learning was evaluated by the area under the curve (AUC) of the receiver-operating characteristic (ROC) analysis. Results The diagnostic prediction model of CAD had been generated according to the user's accessibility such as the general public or clinician (Model 1–4). The performance of the models has ranged from 0.78 to 0.96 by the AUC of ROC analysis. The prediction accuracy of the models ranged from 70.4% to 88.9%. The performance of the diagnostic prediction model of CAD by machine learning improved as the input information increased. Figure 1. Study Flow Chart Conclusion A diagnostic prediction model of CAD using the machine learning method and the registry database was developed. Further studies are needed to verify our results.

Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method

2019 ◽  
Vol 39 (8) ◽  
pp. 1032-1044 ◽  
Author(s):  
Alind Gupta ◽  
Justin J. Slater ◽  
Devon Boyne ◽  
Nicholas Mitsakakis ◽  
Audrey Béliveau ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Risk Prediction ◽  
Prediction Models ◽  
The United States ◽  
Individual Risk ◽  
Support Vector ◽  
Data Set ◽  
Artery Disease

Objectives. Coronary artery disease (CAD) is the leading cause of death and disease burden worldwide, causing 1 in 7 deaths in the United States alone. Risk prediction models that can learn the complex causal relationships that give rise to CAD from data, instead of merely predicting the risk of disease, have the potential to improve transparency and efficacy of personalized CAD diagnosis and therapy selection for physicians, patients, and other decision makers. Methods. We use Bayesian networks (BNs) to model the risk of CAD using the Z-Alizadehsani data set—a published real-world observational data set of 303 Iranian patients at risk for CAD. We also describe how BNs can be used for incorporation of background knowledge, individual risk prediction, handling missing observations, and adaptive decision making under uncertainty. Results. BNs performed on par with machine-learning classifiers at predicting CAD and showed better probability calibration. They achieved a mean 10-fold area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.04, which was comparable with the performance of logistic regression with L1 or L2 regularization (AUC: 0.92 ± 0.06), support vector machine (AUC: 0.92 ± 0.06), and artificial neural network (AUC: 0.91 ± 0.05). We describe the use of BNs to predict with missing data and to adaptively calculate prognostic values of individual variables under uncertainty. Conclusion. BNs are powerful and versatile tools for risk prediction and health outcomes research that can complement traditional statistical techniques and are particularly useful in domains in which information is uncertain or incomplete and in which interpretability is important, such as medicine.

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