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

Content Provider	Springer Nature : BioMed Central
Author	Wang, Zhicheng Gu, Ying Huang, Lindan Liu, Shuai Chen, Qun Yang, Yunyun Hong, Guolin Ning, Wanshan
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.
Related Links	https://cardiab.biomedcentral.com/counter/pdf/10.1186/s12933-024-02439-0.pdf
Ending Page	17
Page Count	17
Starting Page	1
File Format	HTM / HTML
ISSN	14752840
DOI	10.1186/s12933-024-02439-0
Journal	Cardiovascular Diabetology
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2024-09-28
Access Restriction	Open
Subject Keyword	Diabetes Angiology Cardiology Metabolic indicator Blood routine Biochemical detection Machine learning Cardiovascular disease Circulation system disease
Content Type	Text
Resource Type	Article
Subject	Cardiology and Cardiovascular Medicine Internal Medicine Endocrinology, Diabetes and Metabolism
Journal Impact Factor	8.5/2023
5-Year Journal Impact Factor	8.9/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach

Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: findings from the China Suboptimal Health Cohort

Predictive and diagnostic biomarkers for gestational diabetes and its associated metabolic and cardiovascular diseases

Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach

Machine learning in precision diabetes care and cardiovascular risk prediction

Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review

Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project

Machine learning identification of risk factors for heart failure in patients with diabetes mellitus with metabolic dysfunction associated steatotic liver disease (MASLD): the Silesia Diabetes-Heart Project

Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project

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

Similar Documents

Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach

Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: findings from the China Suboptimal Health Cohort

Predictive and diagnostic biomarkers for gestational diabetes and its associated metabolic and cardiovascular diseases

Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach

Machine learning in precision diabetes care and cardiovascular risk prediction

Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review

Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project

Machine learning identification of risk factors for heart failure in patients with diabetes mellitus with metabolic dysfunction associated steatotic liver disease (MASLD): the Silesia Diabetes-Heart Project

Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project

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