Development and Validation of a Predictive Model for Coronary Artery Disease Using Machine Learning

Wang, Chen; Zhao, Yue; Jin, Bingyu; Gan, Xuedong; Liang, Bin; Xiang, Yu‐Tao; Zhang, Xiaokang; Lu, Zhibing; Zheng, Fang

doi:10.3389/fcvm.2021.614204

Cited by 13 publications

(8 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, maybe the low prevalence of females who smoke in this region is the reason for not seeing smoking status as a contributing variable for CVD prediction. HDL is famous as “good cholesterol”, so low levels of HDL are known as a CVD risk factor [ 15 , 57 ]. However, some studies failed to prove the prevention effect of controlling the levels of HDL on CVD events [ 58 , 59 ].…”

Section: Discussionmentioning

confidence: 99%

Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region

Mehrabani-Zeinabad

Feizi

Sadeghi

et al. 2023

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background Cardiovascular diseases (CVD) are the predominant cause of early death worldwide. Identification of people with a high risk of being affected by CVD is consequential in CVD prevention. This study adopts Machine Learning (ML) and statistical techniques to develop classification models for predicting the future occurrence of CVD events in a large sample of Iranians. Methods We used multiple prediction models and ML techniques with different abilities to analyze the large dataset of 5432 healthy people at the beginning of entrance into the Isfahan Cohort Study (ICS) (1990–2017). Bayesian additive regression trees enhanced with “missingness incorporated in attributes” (BARTm) was run on the dataset with 515 variables (336 variables without and the remaining with up to 90% missing values). In the other used classification algorithms, variables with more than 10% missing values were excluded, and MissForest imputes the missing values of the remaining 49 variables. We used Recursive Feature Elimination (RFE) to select the most contributing variables. Random oversampling technique, recommended cut-point by precision-recall curve, and relevant evaluation metrics were used for handling unbalancing in the binary response variable. Results This study revealed that age, systolic blood pressure, fasting blood sugar, two-hour postprandial glucose, diabetes mellitus, history of heart disease, history of high blood pressure, and history of diabetes are the most contributing factors for predicting CVD incidence in the future. The main differences between the results of classification algorithms are due to the trade-off between sensitivity and specificity. Quadratic Discriminant Analysis (QDA) algorithm presents the highest accuracy (75.50 ± 0.08) but the minimum sensitivity (49.84 ± 0.25); In contrast, decision trees provide the lowest accuracy (51.95 ± 0.69) but the top sensitivity (82.52 ± 1.22). BARTm.90% resulted in 69.48 ± 0.28 accuracy and 54.00 ± 1.66 sensitivity without any preprocessing step. Conclusions This study confirmed that building a prediction model for CVD in each region is valuable for screening and primary prevention strategies in that specific region. Also, results showed that using conventional statistical models alongside ML algorithms makes it possible to take advantage of both techniques. Generally, QDA can accurately predict the future occurrence of CVD events with a fast (inference speed) and stable (confidence values) procedure. The combined ML and statistical algorithm of BARTm provide a flexible approach without any need for technical knowledge about assumptions and preprocessing steps of the prediction procedure.

show abstract

Section: Discussionmentioning

confidence: 99%

Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region

Mehrabani-Zeinabad

Feizi

Sadeghi

et al. 2023

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…Due to its high efficiency and accuracy, random forest algorithms as a type of ensemble learning method have been successful in the prediction and identification of clinical disease ( Savargiv et al, 2021 ). Wang et al developed a model to predict coronary artery disease using a random forest algorithm, which had good discrimination abilities ( Wang et al, 2021 ). Another study demonstrated that endobronchial optical coherence tomography based on a random forest algorithm was effective for the detection of early malignant pulmonary disease ( Ding et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Construction of a neural network diagnostic model and investigation of immune infiltration characteristics for Crohn’s disease

Yang

Qiao

et al. 2022

Front. Genet.

View full text Add to dashboard Cite

Objective: Crohn's disease (CD), a chronic recurrent illness, is a type of inflammatory bowel disease whose incidence and prevalence rates are gradually increasing. However, there is no universally accepted criterion for CD diagnosis. The aim of this study was to create a diagnostic prediction model for CD and identify immune cell infiltration features in CD.Methods: In this study, gene expression microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Then, we identified differentially expressed genes (DEGs) between 178 CD and 38 control cases. Enrichment analysis of DEGs was also performed to explore the biological role of DEGs. Moreover, the "randomForest" package was applied to select core genes that were used to create a neural network model. Finally, in the training cohort, we used CIBERSORT to evaluate the immune landscape between the CD and normal groups. Results:The results of enrichment analysis revealed that these DEGs may be involved in biological processes associated with immunity and inflammatory responses. Moreover, the top 3 hub genes in the protein-protein interaction network were IL-1β, CCL2, and CXCR2. The diagnostic model allowed significant discrimination with an area under the ROC curve of 0.984 [95% confidence interval: 0.971-0.993]. A validation cohort (GSE36807) was utilized to ensure the reliability and applicability of the model. In addition, the immune infiltration analysis indicated nine different immune cell types were significantly different between the CD and healthy control groups. Conclusion:In summary, this study offers a novel insight into the diagnosis of CD and provides potential biomarkers for the precise treatment of CD.

show abstract

“…RF is a supervised ensemble learning method and based on decision trees that were built from the variable set. RF performs well in solving the overfitting problem of unbalanced data [ 16 ]. XGBoost is another ensemble tree algorithm.…”

Section: Methodsmentioning

confidence: 99%

Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge

Tian

Han

et al. 2023

Health Qual Life Outcomes

View full text Add to dashboard Cite

Background Patient-reported outcomes (PROs) can be obtained outside hospitals and are of great significance for evaluation of patients with chronic heart failure (CHF). The aim of this study was to establish a prediction model using PROs for out-of-hospital patients. Methods CHF-PRO were collected in 941 patients with CHF from a prospective cohort. Primary endpoints were all-cause mortality, HF hospitalization, and major adverse cardiovascular events (MACEs). To establish prognosis models during the two years follow-up, six machine learning methods were used, including logistic regression, random forest classifier, extreme gradient boosting (XGBoost), light gradient boosting machine, naive bayes, and multilayer perceptron. Models were established in four steps, namely, using general information as predictors, using four domains of CHF-PRO, using both of them and adjusting the parameters. The discrimination and calibration were then estimated. Further analyze were performed for the best model. The top prediction variables were further assessed. The Shapley additive explanations (SHAP) method was used to explain black boxes of the models. Moreover, a self-made web-based risk calculator was established to facilitate the clinical application. Results CHF-PRO showed strong prediction value and improved the performance of the models. Among the approaches, XGBoost of the parameter adjustment model had the highest prediction performance with an area under the curve of 0.754 (95% CI: 0.737 to 0.761) for death, 0.718 (95% CI: 0.717 to 0.721) for HF rehospitalization and 0.670 (95% CI: 0.595 to 0.710) for MACEs. The four domains of CHF-PRO, especially the physical domain, showed the most significant impact on the prediction of outcomes. Conclusion CHF-PRO showed strong prediction value in the models. The XGBoost models using variables based on CHF-PRO and the patient’s general information provide prognostic assessment for patients with CHF. The self-made web-based risk calculator can be conveniently used to predict the prognosis for patients after discharge. Clinical Trial Registration URL: http://www.chictr.org.cn/index.aspx; Unique identifier: ChiCTR2100043337. Graphical abstract

show abstract

Development and Validation of a Predictive Model for Coronary Artery Disease Using Machine Learning

Cited by 13 publications

References 51 publications

Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region

Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region

Construction of a neural network diagnostic model and investigation of immune infiltration characteristics for Crohn’s disease

Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge

Contact Info

Product

Resources

About