Construction of machine learning diagnostic models for cardiovascular pan-disease based on blood routine and biochemical detection data

Abstract

Background: Cardiovascular disease, also known as circulation system disease, remains the leading cause of morbidity and mortality worldwide. Traditional methods for diagnosing cardiovascular disease are often expensive and time-consuming. So the purpose of this study is to construct machine learning models for the diagnosis of cardiovascular diseases using easily accessible blood routine and biochemical detection data and explore the unique hematologic features of cardiovascular diseases, including some metabolic indicators.

Methods: After the data preprocessing, 25,794 healthy people and 32,822 circulation system disease patients with the blood routine and biochemical detection data were utilized for our study. We selected logistic regression, random forest, support vector machine, eXtreme Gradient Boosting (XGBoost), and deep neural network to construct models. Finally, the SHAP algorithm was used to interpret models.

Results: The circulation system disease prediction model constructed by XGBoost possessed the best performance (AUC: 0.9921 (0.9911-0.9930); Acc: 0.9618 (0.9588-0.9645); Sn: 0.9690 (0.9655-0.9723); Sp: 0.9526 (0.9477-0.9572); PPV: 0.9631 (0.9592-0.9668); NPV: 0.9600 (0.9556-0.9644); MCC: 0.9224 (0.9165-0.9279); F1 score: 0.9661 (0.9634-0.9686)). Most models of distinguishing various circulation system diseases also had good performance, the model performance of distinguishing dilated cardiomyopathy from other circulation system diseases was the best (AUC: 0.9267 (0.8663-0.9752)). The model interpretation by the SHAP algorithm indicated features from biochemical detection made major contributions to predicting circulation system disease, such as potassium (K), total protein (TP), albumin (ALB), and indirect bilirubin (NBIL). But for models of distinguishing various circulation system diseases, we found that red blood cell count (RBC), K, direct bilirubin (DBIL), and glucose (GLU) were the top 4 features subdividing various circulation system diseases.

Conclusions: The present study constructed multiple models using 50 features from the blood routine and biochemical detection data for the diagnosis of various circulation system diseases. At the same time, the unique hematologic features of various circulation system diseases, including some metabolic-related indicators, were also explored. This cost-effective work will benefit more people and help diagnose and prevent circulation system diseases.

Keywords: Biochemical detection; Blood routine; Cardiovascular disease; Circulation system disease; Machine learning; Metabolic indicator.

Fig. 1

The flow chart of this study

Fig. 2

Construction of circulation system disease prediction model using clinical blood samples. (A) The AUC of 69 circulation system disease prediction models. ROC curves of five machine learning methods using different data. (B) Blood routine combined with biochemical detection. (C) Blood routine. (D) Biochemical detection

Fig. 3

The AUC of 69 models distinguishes a kind of circulation system disease from others

Fig. 4

The top 20 features for the circulation system disease prediction model using different data. (A) Blood routine. (B) Biochemical detection. (C) Blood routine combined with biochemical detection. The red represents a high value, and the blue represents a low value. If the SHAP value is positive, it represents the positive effect of the feature on the model, and vice versa. All features are listed in order of importance from top to bottom. (D) The joyplot of numerical distributions of K, TP, ALB, and NBIL among various circulation system diseases and healthy people

Fig. 5

Analysis of specific indicators for differentiation between different circulation system diseases. (A) The heatmap displays SHAP values of 50 features for each disease differentiation model. The positive SHAP value is added to the absolute value of the negative SHAP value to form the final SHAP value to be displayed. (B) The network shows the intersection top 10 features among different disease differentiation models. The red circles represent various circulation system diseases, and the blue circles represent various features. The larger the blue circle, the more the intersection features. (C) The joyplot of numerical distributions of RBC, K, DBIL, and GLU among various circulation system diseases and healthy people