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Framingham risk score conventional risk factors are potent to predict all-cause mortality using machine learning algorithms: a population-based prospective cohort study over 40 years in China

Dataset

  • QianQian Huang (Creator)
  • TianShu Zeng (Creator)
  • JiaoYue Zhang (Creator)
  • Jie Min (Creator)
  • Juan Zheng (Creator)
  • ShengHua Tian (Creator)
  • Hantao Huang (Creator)
  • XiaoHuan Liu (Creator)
  • Hao ZhangDepartment of Cardiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing , China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing , China, Nanjing Agricultural University, First Affiliated Hospital of Harbin Medical University, Jiangnan University, Ruian People's Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Shanghai Stomatological Hospital, Jilin University, Nanyang Technological University, Henan Normal University, Northwestern Polytechnical University Xian, Jiangsu Province Hospital, South China University of Technology, Cornell University, Ocean University of China, Jinan University, McGill University, Chongqing Medical University, Shanghai Jiao Tong University, Chinese Academy of Sciences, Second Affiliated Hospital of Harbin Medical University, Nanjing Medical University, Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China., Harvard University, Heilongjiang Provincial Hospital, Department of Pathology, The First Affiliated Hospital of Hainan Medical University, Haikou, China;, Hubei Academy of Agricultural Sciences, Shanghai Children's Medical Center, University of Arizona, Second Affiliated Hospital of Nanjing Medical University, Huazhong University of Science and Technology, University of Victoria, University of Chinese Academy of Sciences, Sichuan University, Changhai Hospital, Guangdong Pharmaceutical University, Zhejiang University, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Dalian University of Technology, Yunnan Academy of Agricultural Sciences, Zhongnan Hospital of Wuhan University, Nanchang University (Creator)
  • Ping WangCAS - Beijing Institute of Genomics, Tianjin Medical University, Northwell Health System, Feinstein Institutes for Medical Research, Affiliated Hospital of Guizhou Medical University, University of Chinese Academy of Sciences, University of Minnesota, Chinese Academy of Medical Sciences & Peking Union Medical College, Chinese Academy of Agricultural Sciences, Urology Department The Affiliated Qingdao Central Hospital of Qingdao University, The Second Affiliated Hospital of Medical College of Qingdao University Qingdao Shandong China, Jilin Agricultural University, Guangzhou Medical University, CAS - Shanghai Institute of Materia Medica, Donald & Barbara Zucker School of Medicine at Hofstra/Northwell, Xiyuan Hospital, Tianjin Medical University Cancer Institute and Hospital, Southern Medical University, Beijing Tongren Hospital, First Hospital of Jilin University, Lanzhou University, Affiliated Hospital of North Sichuan Medical College, China Academy of Chinese Medical Sciences, Tongji University, University of Science and Technology of China, China Medical University, Third Affiliated Hospital of Sun Yat-sen University, Zhejiang University, East Carolina University, Louisiana State University Health Sciences Center New Orleans, Capital Medical University, Zhongnan University of Economics and Law, Jackson Laboratory (Creator)
  • Xiang Hu (Creator)
  • LuLu Chen (Creator)

Description

Predicting all-cause mortality using available or conveniently modifiable risk factors is potentially crucial in reducing deaths precisely and efficiently. Framingham risk score (FRS) is widely used in predicting cardiovascular diseases, and its conventional risk factors are closely pertinent to deaths. Machine learning is increasingly considered to improve the predicting performances by developing predictive models. We aimed to develop the all-cause mortality predictive models using five machine learning (ML) algorithms (decision trees, random forest, support vector machine (SVM), XgBoost, and logistic regression) and determine whether FRS conventional risk factors are sufficient for predicting all-cause mortality in individuals over 40 years. Our data were obtained from a 10-year population-based prospective cohort study in China, including 9143 individuals over 40 years in 2011, and 6879 individuals followed-up in 2021. The all-cause mortality prediction models were developed using five ML algorithms by introducing all features available (182 items) or FRS conventional risk factors. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the predictive models. The AUC and 95% confidence interval of the all-cause mortality prediction models developed by FRS conventional risk factors using five ML algorithms were 0.75 (0.726–0.772), 0.78 (0.755–0.799), 0.75 (0.731–0.777), 0.77 (0.747–0.792), and 0.78 (0.754–0.798), respectively, which is close to the AUC values of models established by all features (0.79 (0.769–0.812), 0.83 (0.807–0.848), 0.78 (0.753–0.798), 0.82 (0.796–0.838), and 0.85 (0.826–0.866), respectively). Therefore, we tentatively put forward that FRS conventional risk factors were potent to predict all-cause mortality using machine learning algorithms in the population over 40 years.
Date made available2023
PublisherSAGE Journals

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