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

Abstract

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.

Inteligencia artificial. Aplicación en las imágenes cardiovasculares y la prevención cardiovascular

“…Estos estudios, desarrollados en diferentes regiones del mundo, evaluaron múltiples algoritmos con diferentes técnicas de IA, los cuales incluyeron muchas más variables que las utilizadas habitualmente para estratificar el riesgo cardiovascular. (75)(76)(77)(78) En líneas generales, estos modelos, que emplearon datos de miles de pacientes, han demostrado una muy buena capacidad predictiva, y en algunos casos, superior a los puntajes clásicos como el puntaje de Framingham. (75) Asimismo, otro modelo basado en IA que tomó en cuenta la presencia de ateromatosis subclínica (análisis cualitativo de placas ateroscleróticas por TC), tuvo un valor pronóstico considerable para detectar eventos cardiovasculares mayores, y mostró un valor adicional sobre los factores de riesgo clínicos, el puntaje de calcio coronario o los métodos tradicionales para evaluar las imágenes de la TC.…”

Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study in 2023

“…Several risk factors with nonlinear interactions are among the complicated linkages that these models tend to oversimplify [ 14 ]. Prediction models based on machine learning algorithms are robust against common limitations such as nonlinearity, multicollinearity, interaction, and complexities available in large datasets in traditional statistical models [ 16 ]. Moreover, it is envisaged that prediction models based on machine-learning algorithms demonstrate better predictive performance than traditional statistical methods [ 16 ].…”

“…Prediction models based on machine learning algorithms are robust against common limitations such as nonlinearity, multicollinearity, interaction, and complexities available in large datasets in traditional statistical models [ 16 ]. Moreover, it is envisaged that prediction models based on machine-learning algorithms demonstrate better predictive performance than traditional statistical methods [ 16 ]. For this reason, machine learning approaches have shown great promise in supporting clinical decision-making, helping create clinical guidelines and management algorithms, and encouraging the adoption of clinical practices based on evidence for the treatment of cardiovascular diseases (CVDs) [ 13 ].…”