Development and verification of prediction models for preventing cardiovascular diseases

English version

Autoři: Ji Min Sung ^aff001; In-Jeong Cho ^aff002; David Sung ^aff003; Sunhee Kim ^aff004; Hyeon Chang Kim ^aff005; Myeong-Hun Chae ^aff007; Maryam Kavousi ^aff008; Oscar L. Rueda-Ochoa ^aff008; M. Arfan Ikram ^aff008; Oscar H. Franco ^aff008; Hyuk-Jae Chang ^aff005
Působiště autorů: Integrative Research Center for Cerebrovascular and Cardiovascular diseases, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea ^aff001; Division of Cardiology, Ewha University College of Medicine, Seoul, Korea ^aff002; Data Science Team of KT NexR, Seoul, Korea ^aff003; Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea ^aff004; Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Korea ^aff005; Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Korea ^aff006; AI R&D Lab. of Selvas AI Inc., Seoul, Korea ^aff007; Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands ^aff008; School of Medicine, Faculty of Health, Universidad Industrial de Santander UIS, Bucaramanga, Colombia ^aff009; Department of Radiology, Erasmus MC, Rotterdam, the Netherlands ^aff010; Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Korea ^aff011
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222809

Souhrn

Objectives

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.

Methods and findings

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.

Conclusion

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.

Klíčová slova:

Computer and information sciences – Artificial intelligence – Machine learning – Deep learning – Medicine and health sciences – Cardiovascular medicine – Cardiovascular diseases – Epidemiology – Medical risk factors – Vascular medicine – Blood pressure – Public and occupational health – Health screening – Endocrinology – Endocrine disorders – Metabolic disorders – Research and analysis methods – Mathematical and statistical techniques – Statistical methods – Simulation and modeling – Physical sciences – Mathematics – Statistics – Applied mathematics – Algorithms

Zdroje

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Development and verification of prediction models for preventing cardiovascular diseases

Souhrn

Objectives

Methods and findings

Conclusion

Klíčová slova:

Zdroje

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