Cardiovascular Disease Prediction Models Research Articles

Effect of Random Under sampling, Oversampling, and SMOTE on the Performance of Cardiovascular Disease Prediction Models

Cardiovascular Disease (CVD) or commonly known as Heart Disease is a leading cause of mortality globally, prompting extensive research into predictive models to assess individual risk and plan preventive measures. Machine learning approaches such as Random Forest, Support Vector Machine (SVM), and LASSO Logistic Regression have showed promise. Recent studies have indicated that traditional resampling methods like Random Oversampling, Random Undersampling, and SMOTE may not significantly improve model discrimination. This study aims to evaluate the impact of these techniques on the performance of Cardiovascular Disease (CVD) prediction models, utilizing data from the UCI Machine Learning Heart Disease database. By employing LASSO Logistic Regression, Random Forest, and Support Vector Machine (SVM) with resampling techniques, including Random Oversampling, Random Undersampling, and SMOTE. This research seeks to enhance understanding of model performance in addressing class imbalances within the dataset and contribute to refining cardiovascular disease (CVD) prediction strategies. This study demonstrates that the use of the SMOTE technique significantly enhances the performance of cardiovascular disease (CVD) prediction models. Specifically, when combined with the Random Forest algorithm, SMOTE achieves the best performance in terms of accuracy, sensitivity, and specificity. This highlights the importance of selecting appropriate resampling techniques to handle class imbalance in datasets. Consequently, this research contributes to refining CVD prediction strategies and provides new insights into improving prediction accuracy in imbalanced medical data.

Age and sex specific thresholds for risk stratification of cardiovascular disease and clinical decision making: prospective open cohort study

ObjectiveTo quantify the potential advantages of using 10 year risk prediction models for cardiovascular disease, in combination with risk thresholds specific to both age and sex, to identify individuals at...

The Application of Mendelian Randomization in Cardiovascular Disease Risk Prediction: Current Status and Future Prospects.

Cardiovascular disease (CVD), a leading cause of death and disability worldwide, and is associated with a wide range of risk factors, and genetically associated conditions. While many CVDs are preventable and early detection alongside treatment can significantly mitigate complication risks, current prediction models for CVDs need enhancements for better accuracy. Mendelian randomization (MR) offers a novel approach for estimating the causal relationship between exposure and outcome by using genetic variation in quasi-experimental data. This method minimizes the impact of confounding variables by leveraging the random allocation of genes during gamete formation, thereby facilitating the integration of new predictors into risk prediction models to refine the accuracy of prediction. In this review, we delve into the theory behind MR, as well as the strengths, applications, and limitations behind this emerging technology. A particular focus will be placed on MR application to CVD, and integration into CVD prediction frameworks. We conclude by discussing the inclusion of various populations and by offering insights into potential areas for future research and refinement.

Comparative analysis of supervised learning algorithms for prediction of cardiovascular diseases.

With the advent of artificial intelligence technology, machine learning algorithms have been widely used in the area of disease prediction. Cardiovascular disease (CVD) seriously jeopardizes human health worldwide, thereby needing the establishment of an effective CVD prediction model that can be of great significance for controlling the risk of the disease and safeguarding the physical and mental health of the population. Considering the UCI heart disease dataset as an example, initially, a single machine learning prediction model was constructed. Subsequently, six methods such as Pearson, chi-squared, RFE and LightGBM were comprehensively used for the feature screening. On the basis of the base classifiers, Soft Voting fusion and Stacking fusion was carried out to build a prediction model for cardiovascular diseases, in order to realize an early warning and disease intervention for high-risk populations. To address the data imbalance problem, the SMOTE method was adopted to process the data set, and the prediction effect of the model was analyzed using multi-dimensional and multi-indicators. In the single classifier model, the MLP algorithm performed optimally on the preprocessed heart disease dataset. After feature selection, five features eliminated. The ENSEM_SV algorithm that combines the base classifiers to determine the prediction results by soft voting on the results of the classifiers achieved the optimal value on five metrics such as Accuracy, Jaccard_Score, Hamm_Loss, AUC, etc., and the AUC value reached 0.951. The RF, ET, GBDT, and LGB algorithms were employed in the first stage sub-model composed of base classifiers. The AB algorithm was selected as the second stage model, and the ensemble algorithm ENSEM_ST, obtained by Stacking fusion of the two stages exhibited the best performance on 7 indicators such as Accuracy, Sensitivity, F1_Score, Mathew_Corrcoef, etc., and the AUC reached 0.952. Furthermore, a comparison of the algorithms' classification effects based on different training set occupancy was carried out. The results indicated that the prediction performance of both the fusion models was better than the single models, and the overall effect of ENSEM_ST fusion was stronger than the ENSEM_SV fusion. The fusion model established in this study improved the overall classification accuracy and stability of the model to a significant extent. It has a good application value in the predictive analysis of CVD diagnosis, and can provide a valuable reference in the disease diagnosis and intervention strategies.

Advanced Cloud-Based Prediction Models for Cardiovascular Disease: Integrating Machine Learning and Feature Selection Techniques

Advanced Cloud-Based Prediction Models for Cardiovascular Disease: Integrating Machine Learning and Feature Selection Techniques

Efficient Machine Learning Techniques to Classifying Cardiovascular Disease and Improve Prediction Analysis

Cardiovascular Disease (CVD) account for a large portion of the global health burden and are one of the main causes of decease worldwide. In the classification and forecasting of CVDs, Machine Learning (ML) techniques have demonstrated encouraging outcomes. In this research report, a comparative analysis of classification and prediction models for CVD is presented, including both linear and ensemble ML approaches. The paper compares ensemble models like Catboost, Histogram Gradient Boosting Machine (HGBM), and Extra Trees against linear models like Gaussian Nave Bayes, SVM, and KNN. The objective is to identify the most effective CVD prediction model by assessing its performance through accuracy, precision, sensitivity and F1 score as key evaluation metrics. Moreover, results show that ensemble models outperform linear models using advanced techniques such as boosting and histogram-based algorithms. The results underscore the critical role ensemble models play in accurately diagnosing and predicting cardiovascular disease and provide important new information to researchers and healthcare providers. Using these models has the potential to significantly improve patient outcomes and health management by enabling early detection and intervention

Prediction and risk analysis of Cardio Vascular diseases in IoHT by enhanced CHIO-based Residual and dilated gated network with Attention Mechanism

Generally, computer networks automatically discover data based on the machine learning process. It is the execution of Artificial Intelligence (AI) without any specific program. Using both machine learning and AI, an effective optimization was done for Cardio Vascular Disease (CVD). CVD is associated with disorders that lead to hypertension and unexpected cardiac arrest. To promote good health and prevent risky situations, the Internet of Health Things (IoHT) helps to enhance the treatment's effectiveness. The most challenging task is to perform accurate heart disease prediction. Thus, a CVD prediction model is designed. At first, the needed information is collected from the benchmark dataset. After that, the pre-processing is done on the collected data using the data cleaning and scaling method. The pre-processed data are sent to the CVD prediction phase with the aid of Adaptive Residual and Dilated Long Short Term Memory with Attention Mechanism (ARDL-AM) and the parameters in this structure are optimized by the Enhanced Coronavirus Herd Immunity Optimizer (ECHIO). The obtained results from the ARDL-AM are used for the CVD prediction process. The simulations were carried out with various measures to calculate the recommended method's efficiency.

Machine learning-based prediction model for myocardial ischemia under high altitude exposure: a cohort study

High altitude exposure increases the risk of myocardial ischemia (MI) and subsequent cardiovascular death. Machine learning techniques have been used to develop cardiovascular disease prediction models, but no reports exist for high altitude induced myocardial ischemia. Our objective was to establish a machine learning-based MI prediction model and identify key risk factors. Using a prospective cohort study, a predictive model was developed and validated for high-altitude MI. We consolidated the health examination and self-reported electronic questionnaire data (collected between January and June 2022 in 920th Joint Logistic Support Force Hospital of china) of soldiers undergoing high-altitude training, along with the health examination and second self-reported electronic questionnaire data (collected between December 2022 and January 2023) subsequent to their completion on the plateau, into a unified dataset. Participants were subsequently allocated to either the training or test dataset in a 3:1 ratio using random assignment. A predictive model based on clinical features, physical examination, and laboratory results was designed using the training dataset, and the model's performance was evaluated using the area under the receiver operating characteristic curve score (AUC) in the test dataset. Using the training dataset (n = 2141), we developed a myocardial ischemia prediction model with high accuracy (AUC = 0.86) when validated on the test dataset (n = 714). The model was based on five laboratory results: Eosinophils percentage (Eos.Per), Globulin (G), Ca, Glucose (GLU), and Aspartate aminotransferase (AST). Our concise and accurate high-altitude myocardial ischemia incidence prediction model, based on five laboratory results, may be used to identify risks in advance and help individuals and groups prepare before entering high-altitude areas. Further external validation, including female and different age groups, is necessary.

A Klotho-Based Machine Learning Model for Prediction of both Kidney and Cardiovascular Outcomes in Chronic Kidney Disease

Introduction: This study aimed to develop and validate machine learning (ML) models based on serum Klotho for predicting end-stage kidney disease (ESKD) and cardiovascular disease (CVD) in patients with chronic kidney disease (CKD). Methods: Five different ML models were trained to predict the risk of ESKD and CVD at three different time points (3, 5, and 8 years) using a cohort of 400 non-dialysis CKD patients. The dataset was divided into a training set (70%) and an internal validation set (30%). These models were informed by data comprising 47 clinical features, including serum Klotho. The best-performing model was selected and used to identify risk factors for each outcome. Model performance was assessed using various metrics. Results: The findings showed that the least absolute shrinkage and selection operator regression model had the highest accuracy (C-index = 0.71) in predicting ESKD. The features mainly included in this model were estimated glomerular filtration rate, 24-h urinary microalbumin, serum albumin, phosphate, parathyroid hormone, and serum Klotho, which achieved the highest area under the curve (AUC) of 0.930 (95% CI: 0.897–0.962). In addition, for the CVD risk prediction, the random survival forest model with the highest accuracy (C-index = 0.66) was selected and achieved the highest AUC of 0.782 (95% CI: 0.633–0.930). The features mainly included in this model were age, history of primary hypertension, calcium, tumor necrosis factor-alpha, and serum Klotho. Conclusion: We successfully developed and validated Klotho-based ML risk prediction models for CVD and ESKD in CKD patients with good performance, indicating their high clinical utility.

The Comparative Early Prediction Model for Cardiovascular Disease Using Machine Learning

Cardiovascular disease (CVD) is a leading cause of death and a major contributor to disability. Early detection of cardiovascular disease using ANFIS has the potential to reduce costs and simplify treatment. This study aims to develop a prediction model using ANFIS (Adaptive Neuro-Fuzzy Inference System) for early detection of cardiovascular disease. The dataset used consists of 500 data with 12 features, including various risk factors such as blood sugar levels, cholesterol, uric acid, systolic blood pressure, diastolic blood pressure, body mass index (BMI), age, smoking habits, lifestyle, genetic factors, and gender, and one label feature. This study compares cardiovascular disease prediction models using machine learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and ANFIS. The development of the KNN algorithm involves the value of K=5 with the Euclidian distance measure. The SVM algorithm used a kernel cache of 200 and a convergence epsilon of 0.001. The ANFIS model was built using 500 data sets divided into training (70%) and testing (30%) data, with learning rate variations of 0.01, 0.05, 0.1, 0.2, and 0.5. The results of testing the early detection model show for SVM, the accuracy value is 0.760, the precision value is 0.839, and the recall value is 0.671. For the KNN model, the accuracy value is 0.758, the precision value is 0.768, and the recall value is 0.771. As for the ANFIS model, the accuracy value reaches 0.989, precision value 0.996, and recall value 0.988. The model using ANFIS has the highest performance. Further study of the model using ANFIS with learning rate variations shows that a learning rate of 0.1 provides the most optimal performance.

The relevance of competing risk adjustment in cardiovascular risk prediction models for clinical practice.

Many models developed for predicting the risk of cardiovascular disease (CVD), are adjusted for the competing risk of non-CVD mortality, which has been suggested to reduce potential overestimation of cumulative incidence in populations where the risk of competing events is high. The objective was to evaluate and illustrate the clinical impact of competing risk adjustment when deriving a CVD prediction model in a high-risk population. Individuals with established atherosclerotic CVD were included from the Utrecht Cardiovascular Cohort - Secondary Manifestations of ARTerial disease (UCC-SMART). In 8,355 individuals, followed for median of 8.2 years (IQR 4.2-12.5), two similar prediction models for the estimation of 10-year residual CVD risk were derived: with competing risk adjustment using a Fine and Gray model and without competing risk adjustment using a Cox proportional hazards model. On average, predictions were higher from the Cox model. The Cox model predictions overestimated the cumulative incidence ((predicted-observed ratio 1.14 [95%CI 1.09-1.20), which was most apparent in the highest risk quartiles and in older persons. Discrimination of both models was similar. When determining treatment eligibility on thresholds of predicted risks, more individuals would be treated based on the Cox model predictions. If, for example, individuals with a predicted risk >20% were considered eligible for treatment, 34% of the population would be treated according to the Fine and Gray model predictions and 44% according to the Cox model predictions. Individual predictions from the model unadjusted for competing risks were higher, reflecting the different interpretations of both models. For models aiming to accurately predict absolute risks, especially in high-risk populations, competing risk adjustment must be considered.

Enhanced cardiovascular disease prediction model using random forest algorithm

Cardiovascular diseases (CVDs) such as hypertension, heart failure, stroke, and coronary artery disease are now the major causes of early death worldwide, particularly in low and middle-income countries. Early detection of these disorders could lower the number of people who die prematurely. Researchers have proposed many techniques for CVD prediction, such as data mining, machine learning (ML), and the Internet of Things (IoT), for the early detection and monitoring of cardiac patients. Although these techniques are suggested and sometimes used, there is still much worry regarding their efficacy in situations where the error rate is high and accuracy is doubtful. As a result, it is necessary to select a prediction technique that can deliver more accuracy and fewer errors. This paper proposes an effective ensemble method based on the Random Forest (RF) algorithm for improving accuracy by combining multiple feature selection techniques. A classification model is built using training datasets and produces several decision trees. These datasets adjust for any missing data and include estimates of important classification variables. Data preparation and feature selection approaches such as correlation coefficients and data mining feature selection techniques are applied to remove outliers. Finally, a cardiovascular disease prediction model is created, and the model's increased performance accuracy is tested using a confusion matrix. A dataset from the Kaggle repository with 1025 data points and 14 attributes is used. The saved data are then preprocessed, yielding a dataset of 769 records and 13 variables that are evaluated using RF and compared to various models such as K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Logistic Regression (LR) for the prediction of heart disorders. After employing several techniques, the proposed model achieves an accuracy of 99%, which shows a significant improvement over the other assessed models.

Cardiovascular disease risk prediction via machine learning using mental health data

Abstract Background Robust and accurate risk prediction models are much needed in cardiovascular disease. It is well-known that mental health is associated with the risk of developing cardiovascular disease. It is unknown whether mental health markers can enhance existing risk prediction models for cardiovascular disease. Purpose The main purpose of this study was to assess capability of mental health factors along with traditional risk factors to be used in cardiovascular predictive machine learning models, and to develop a combined machine learning approach using both traditional risk and psychological factors in 375,145 participants of the UK Biobank. Methods A comprehensive Pearson correlation analysis is carried out on UK Biobank data. Subsequently, an ensemble model containing decision tree, random forest, XGBoost, support vector machine (SVM), and deep neural network (DNN) classification approaches was built to predict cardiovascular diseases (CVD) in UK Biobank participants. The model was first trained using traditional cardiovascular risk factors, and subsequently trained using a combination of cardiovascular risk and psychological factors. Results The correlation analysis revealed that there is a correlation between CVD and mental health factors suggesting the potential of mental health application for machine learning models. Our ensemble machine learning model was able to predict CVD with an accuracy of 73.49% using CVD risk factors alone. However, by combining psychological factors with CVD risk factors in the training data, an improved accuracy of 95.70% was achieved. The accuracy and robustness of ensemble machine learning model outperformed any of five constituent learning algorithms alone. Conclusions Our results suggest that mental health assessment data along with traditional risk factors provides a powerful, safe and affordable machine learning model enrichment that can be used for state-of-the-art prediction of CVD. Funding Acknowledgement Type of funding sources: None.

Current Cardiovascular Disease Risk Prediction Models Are Not Applicable in MDS Patients: Preliminary Results of a Prospective Observational Single-Centre Cohort Study

Background/aims Cardiovascular disease (CVD) is a leading cause of death in lower-risk Myelodysplastic syndrome (LR-MDS) patients and a shared pathobiology between MDS and CVD has been postulated. However, current evidence emanates only from retrospective cohorts suffering from crucial shortcomings. Prospective annotation can overcome these limitations and delineate the role of MDS as an independent risk factor for CVD. For this reason we conducted a prospective observational single-centre cohort study in LR-MDS patients. Methods Patients underwent thorough evaluation for CVD every 6 months. Cerebrovascular, peripheral and coronary vascular beds were assessed for atherosclerosis by ultrasound and coronary artery calcium (CAC), respectively. Cardiac structure and function was assessed by echocardiography. Multi-Ethnic Study of Atherosclerosis (MESA), Framingham (FRS) risk scores and serum markers of myocardial injury (Serum high-sensitivity troponin T, hsTnT), stress (N-terminal pro-B-type natriuretic peptide, NT-proBNP), and systemic inflammation (high-sensitivity C-reactive protein, hsCRP) were also estimated at each visit. Chi-square, Wilcoxon signed-ranks tests and Pearson's correlation coefficient were used as appropriate. Multiple linear regression was applied for modeling the relationship between a dependent and one or more independent variables. The level of statistical significance was set to P=0.05. Results Table 1 presents baseline characteristics of 26 patients included so far. There were no correlations of CAC, MESA and FRS with either MDS subtype or IPSS-R risk category after adjustment for preexisting CVD, age, sex, and transfusion dependence. In sharp contrast to the general population (Okwuosa, TM et al. JACC 2011), CAC score was not associated with FRS (p=0.7) and was disproportionately increased in low and very low FRS risk patients (Figure 1). Likewise, the number of affected vascular beds (AVB) did not correlate with FRS (p=0,51) (Figure 2), further suggesting that the current CVD risk prediction tools are inaccurate in MDS and arguing for an intrinsic tendency of MDS towards CVD development. A second and a third evaluation was available in 17 and 7 patients, respectively. All patients increased significantly the CAC score both at 6 months (p=0.04) and 1 year (p=0.003). The average increase from baseline was 26.2% at 6 months and 72.6% at 1 year, markedly higher than expected in the general population (McCullough PA et al. Arch Int Med 2009), and was marginally higher in transfusion dependent patients (p=0.049), but not associated with baseline MESA score, FRS, preexisting CVD, exposure to ESAS, statin therapy, sex. Finally, baseline NT-proBNP levels correlated strongly with CAC score P=0.001 (Figure 3) in line with numerous evidence showing that NT-proBNP can potentially serve as a predictor of CVD risk in both MDS and non-MDS individuals. Conclusion To our knowledge this is the first study in LR-MDS patients assessing longitudinally all critical parameters and providing objective measurements of subclinical atherosclerosis in all vascular beds. Our preliminary results indicate that the established CVD prediction models do not operate properly in LR-MDS patients and reinforce the notion of a pro-atherogenic role of MDS. Accurate assessment of CVD risk and prompt detection of subclinical cardiovascular disease in MDS patients will assist clinical decisions on the introduction of intensive monitoring and preventive use of CVD-specific interventions in these individuals. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Abstract 15589: Evaluating Methods to Mitigate the Bias for Machine Learning-Based Cardiovascular Risk Model

Introduction: Cardiovascular disease (CVD) is the leading cause of death in the U.S. and globally. Predictive models are essential for identifying high CVD-risk patients. Machine learning (ML) models have been used to develop predictive models. However, the dataset generalized for training ML models could involve a systematic bias (e.g., sampling bias), which could bias the model. The study investigated the performance of different methods to reduce bias for 10-year CVD prediction models among demographic groups (White and Black, Male and Female). Methods: We used a large cohort derived from VUMC de-identified EHR data, including outpatients with follow-up visits between 2007 and 2017. Logistic regression (LR), random forests (RF), and gradient boosting trees (GBT) were trained. Predictors included traditional CVD risk factors, labs, prior diagnosis, and medications. Fairness metrics were equal opportunity difference (EOD) - a difference of true positive rate (0 indicates fairness), and disparate impact (DI) - a ratio of predicted CVD (1 indicates fairness) among groups. Three de-biasing methods were applied, removing protected attributes (i.e., race, gender) from the model, resampling the training set by sample size, and by the proportion of CVD. Results: The study included 109, 490 individuals (9,824 CVDs) with mean (SD) age of 47.4 (14.7) years (White 86.3%, Black 13.7%, Male 35.5% and Female 64.5%). Models had a slight bias across race groups but a large EOD and DI across genders (i.e. models has higher prediction accuracy in Male than Female). After debiasing, EOD and DI were significantly improved for genders (Fig. 1). Resampling by size reduced bias while retaining accuracy (e.g. 0.78 AUROC for GBT). Conclusions: Among the VUMC cohort, despite the high proportion of women, ML models had a lower prediction accuracy for the women group. Resampling the training set data reduced the bias, but it should be used with caution depending on the magnitude and nature of the bias.

Cardiovascular disease risk prediction via machine learning using mental health data

Abstract Background Robust and accurate risk prediction models are much needed in cardiovascular disease. It is well-known that mental health is associated with the risk of developing cardiovascular disease. It is unknown whether mental health markers can enhance existing risk prediction models for cardiovascular disease. Purpose The main purpose of this study was to assess capability of mental health factors along with traditional risk factors to be used in cardiovascular predictive machine learning models, and to develop a combined machine learning approach using both traditional risk and psychological factors in 375,145 participants of the UK Biobank. Methods A comprehensive Pearson correlation analysis is carried out on UK Biobank data. Subsequently, an ensemble model containing decision tree, random forest, XGBoost, support vector machine (SVM), and deep neural network (DNN) classification approaches was built to predict cardiovascular diseases (CVD) in UK Biobank participants. The model was first trained using traditional cardiovascular risk factors, and subsequently trained using a combination of cardiovascular risk and psychological factors. Results The correlation analysis revealed that there is a correlation between CVD and mental health factors suggesting the potential of mental health application for machine learning models. Our ensemble machine learning model was able to predict CVD with an accuracy of 73.49% using CVD risk factors alone. However, by combining psychological factors with CVD risk factors in the training data, an improved accuracy of 95.70% was achieved. The accuracy and robustness of ensemble machine learning model outperformed any of five constituent learning algorithms alone. Conclusions Our results suggest that mental health assessment data along with traditional risk factors provides a powerful, safe and affordable machine learning model enrichment that can be used for state-of-the-art prediction of CVD. Funding Acknowledgement Type of funding sources: None.

A Cardiovascular Disease Prediction Model Based on Routine Physical Examination Indicators Using Machine Learning Methods: A Cohort Study.

BackgroundCardiovascular diseases (CVD) are currently the leading cause of premature death worldwide. Model-based early detection of high-risk populations for CVD is the key to CVD prevention. Thus, this research aimed to use machine learning (ML) algorithms to establish a CVD prediction model based on routine physical examination indicators suitable for the Xinjiang rural population.MethodThe research cohort data collection was divided into two stages. The first stage involved a baseline survey from 2010 to 2012, with follow-up ending in December 2017. The second-phase baseline survey was conducted from September to December 2016, and follow-up ended in August 2021. A total of 12,692 participants (10,407 Uyghur and 2,285 Kazak) were included in the study. Screening predictors and establishing variable subsets were based on least absolute shrinkage and selection operator (Lasso) regression, logistic regression forward partial likelihood estimation (FLR), random forest (RF) feature importance, and RF variable importance. The selected subset of variables was compared with L1 regularized logistic regression (L1-LR), RF, support vector machine (SVM), and AdaBoost algorithm to establish a CVD prediction model suitable for this population. The incidence of CVD in this population was then analyzed.ResultAfter 4.94 years of follow-up, a total of 1,176 people were diagnosed with CVD (cumulative incidence: 9.27%). In the comparison of discrimination and calibration, the prediction performance of the subset of variables selected based on FLR was better than that of other models. Combining the results of discrimination, calibration, and clinical validity, the prediction model based on L1-LR had the best prediction performance. Age, systolic blood pressure, low-density lipoprotein-L/high-density lipoproteins-C, triglyceride blood glucose index, body mass index, and body adiposity index were all important predictors of the onset of CVD in the Xinjiang rural population.ConclusionIn the Xinjiang rural population, the prediction model based on L1-LR had the best prediction performance.

Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease.

The medical field has seen a rapid increase in the development of artificial intelligence (AI)-based prediction models. With the introduction of such AI-based prediction model tools and software in cardiovascular patient care, the cardiovascular researcher and healthcare professional are challenged to understand the opportunities as well as the limitations of the AI-based predictions. In this article, we present 12 critical questions for cardiovascular health professionals to ask when confronted with an AI-based prediction model. We aim to support medical professionals to distinguish the AI-based prediction models that can add value to patient care from the AI that does not.

Research on Disease Prediction Method Based on R-Lookahead-LSTM.

Cardiovascular disease is one of the most serious diseases that threaten human health in the world today. Therefore, establishing a high-quality disease prediction model is of great significance for the prevention and treatment of cardiovascular disease. In the feature selection stage, three new strong feature vectors are constructed based on the background of disease prediction and added to the original data set, and the relationship between the feature vectors is analyzed by using the correlation coefficient map. At the same time, a random forest algorithm is introduced for feature selection, and the importance ranking of features is obtained. In order to further improve the prediction effect of the model, a cardiovascular disease prediction model based on R-Lookahead-LSTM is proposed. The model based on the stochastic gradient descent algorithm of the fast weight part of the Lookahead algorithm is optimized and improved to the Rectified Adam algorithm; the Tanh activation function is further improved to the Softsign activation function to promote model convergence; and the R-Lookahead algorithm is used to further optimize the long-term memory network model. Therefore, the long- and short-term memory network model can be better improved so that the model tends to be stable as soon as possible, and it is applied to the cardiovascular disease prediction model.

Improving Cardiovascular Disease Prediction Using Automated Coronary Artery Calcium Scoring from Existing Chest CTs.

Cardiovascular disease (CVD) prediction models are widely used in modern medicine and are incorporated into prominent guidelines. Coronary artery calcium (CAC) is a marker of coronary atherosclerotic disease and has proven utility for predicting cardiovascular disease. Despite this, current guidelines recommend against including CAC scores in CVD prediction models due to the medical and financial costs of acquiring it, and the insufficient evidence concerning its ability to improve existing models. Modern machine learning models are capable of automatically extracting coronary calcium scores from existing chest computed tomography (CT) scans, negating these costs. To determine whether the inclusion of CAC scores, automatically extracted using a machine learning algorithm from chest CTs performed for any reason, improves the performance of the American Heart Association/American College of Cardiology 2013 pooled cohort equations (PCE). A retrospective cohort of patients with available chest CTs prior to an index date (2012) was used to compare the performance of the PCE model and an augmented-PCE model which utilizes the CT-based CAC scores on top of the existing model. The PCE and the augmented-PCE predictions were calculated as of an index date (2012) using data from the electronic health record and existing chest CTs. The performance of both models was evaluated by comparing their predictions to cardiovascular events that occurred during a 5-year follow-up period (until 2017). A total of 14,135 patients aged 40-79years were included in the study, of whom 470 (3.3%) had documented CVD events during the follow-up. The augmented-PCE model showed a significant improvement in c-statistic (0.64 ≥ 0.69, Δ = 0.05, 95% CI: 0.03 to 0.06), sensitivity (53% ≥ 57%, Δ = 4.7%, 95% CI: 0-9.0%), specificity (67% ≥ 70%, Δ = 2.8%, 95% CI: 0.9-5.1%), in positive predictive value (5% ≥ 6%, Δ = 0.9%, 95% CI: 0.4 to 1.4%), negative predictive value (97.7% ≥ 97.9%, Δ = 0.3%, 95% CI: 0.1 to 0.5%), and in the categorical net reclassification index (7.4%, 95% CI: 2.4 to 12.1%). Automatically generated CAC scores from existing CTs can aid in CVD risk determination, improving model performance when used on top of existing predictors. Use of existing CTs avoids most pitfalls currently cited against the routine use of CAC in CVD predictions (e.g., additional radiation exposure), and thus affords a net gain in predictive accuracy.