Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Identifying the Main Risk Factors for CVD Prediction Using Machine Learning Algorithms

Version 1 : Received: 23 August 2021 / Approved: 24 August 2021 / Online: 24 August 2021 (14:00:12 CEST)

A peer-reviewed article of this Preprint also exists.

Guarneros-Nolasco, L.R.; Cruz-Ramos, N.A.; Alor-Hernández, G.; Rodríguez-Mazahua, L.; Sánchez-Cervantes, J.L. Identifying the Main Risk Factors for Cardiovascular Diseases Prediction Using Machine Learning Algorithms. Mathematics 2021, 9, 2537. Guarneros-Nolasco, L.R.; Cruz-Ramos, N.A.; Alor-Hernández, G.; Rodríguez-Mazahua, L.; Sánchez-Cervantes, J.L. Identifying the Main Risk Factors for Cardiovascular Diseases Prediction Using Machine Learning Algorithms. Mathematics 2021, 9, 2537.

Journal reference: Mathematics 2021, 9, 2537
DOI: 10.3390/math9202537

Abstract

CVDs are a leading cause of death globally. In CVDs, the heart is unable to deliver enough blood to other body regions. Since effective and accurate diagnosis of CVDs is essential for CVD prevention and treatment, machine learning (ML) techniques can be effectively and reliably used to discern patients suffering from a CVD from those who do not suffer from any heart condition. Namely, machine learning algorithms (MLAs) play a key role in the diagnosis of CVDs through predictive models that allow us to identify the main risks factors influencing CVD development. In this study, we analyze the performance of ten MLAs on two datasets for CVD prediction and two for CVD diagnosis. Algorithm performance is analyzed on top-two and top-four dataset attributes/features with respect to five performance metrics –accuracy, precision, recall, f1-score, and roc-auc – using the train-test split technique and k-fold cross-validation. Our study identifies the top two and four attributes from each CVD diagnosis/prediction dataset. As our main findings, the ten MLAs exhibited appropriate diagnosis and predictive performance; hence, they can be successfully implemented for improving current CVD diagnosis efforts and help patients around the world, especially in regions where medical staff is lacking.

Keywords

Big data; Health prevention; Machine learning; Medical data

Subject

MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management

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