Development and verification of prediction models for preventing cardiovascular diseases

• Published in: PloS one Vol. 14; no. 9; p. e0222809
• Main Authors: Sung, Ji Min, Cho, In-Jeong, Sung, David, Kim, Sunhee, Kim, Hyeon Chang, Chae, Myeong-Hun, Kavousi, Maryam, Rueda-Ochoa, Oscar L, Ikram, M Arfan, Franco, Oscar H, Chang, Hyuk-Jae
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.09.2019; Public Library of Science (PLoS)
• Subjects: Adult; Algorithms; Blood Pressure; Cardiovascular diseases; Cardiovascular Diseases - diagnosis; Cardiovascular Diseases - physiopathology; Cardiovascular Diseases - prevention & control; Clinical trials; Computer and Information Sciences; Datasets; Death; Deep Learning; Diagnosis; Female; Females; Health; Health risks; Heart diseases; Humans; Insurance; Machine learning; Male; Males; Medical examination; Medical research; Medicine and Health Sciences; Middle Aged; Models, Cardiovascular; National health insurance; Neural networks; Patient outcomes; Physical Sciences; Prediction models; Prevention; Prognosis; Proportional Hazards Models; Regression analysis; Regression models; Research and Analysis Methods; Risk analysis; Risk Assessment - methods; Risk Assessment - statistics & numerical data; Risk Factors; Studies; Survival Analysis; Time dependence; Time series; South Korea; Netherlands
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0222809

Cardiovascular disease (CVD) is one of the major causes of death worldwide. For improved accuracy of CVD prediction, risk classification was performed using national time-series health examination data. The data offers an opportunity to access deep learning (RNN-LSTM), which is widely known as an outstanding algorithm for analyzing time-series datasets. The objective of this study was to show the improved accuracy of deep learning by comparing the performance of a Cox hazard regression and RNN-LSTM based on survival analysis. We selected 361,239 subjects (age 40 to 79 years) with more than two health examination records from 2002-2006 using the National Health Insurance System-National Health Screening Cohort (NHIS-HEALS). The average number of health screenings (from 2002-2013) used in the analysis was 2.9 ± 1.0. Two CVD prediction models were developed from the NHIS-HEALS data: a Cox hazard regression model and a deep learning model. In an internal validation of the NHIS-HEALS dataset, the Cox regression model showed a highest time-dependent area under the curve (AUC) of 0.79 (95% CI 0.70 to 0.87) for in females and 0.75 (95% CI 0.70 to 0.80) in males at 2 years. The deep learning model showed a highest time-dependent AUC of 0.94 (95% CI 0.91 to 0.97) for in females and 0.96 (95% CI 0.95 to 0.97) in males at 2 years. Layer-wise Relevance Propagation (LRP) revealed that age was the variable that had the greatest effect on CVD, followed by systolic blood pressure (SBP) and diastolic blood pressure (DBP), in that order. The performance of the deep learning model for predicting CVD occurrences was better than that of the Cox regression model. In addition, it was confirmed that the known risk factors shown to be important by previous clinical studies were extracted from the study results using LRP.