Composite socio-environmental risk score for cardiovascular assessment: An explainable machine learning approach.

Abstract

Authors

• Zhuo Chen
  State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China. Electronic address: gychenzhuo@aliyun.com.
• Jean-Eudes Dazard
  Harrington Heart and Vascular Institute, University Hospitals, 11100 Euclid Ave, Cleveland, OH 44106, USA.
• Pedro Rafael Vieira de Oliveira Salerno
  Herman K Hellerstein Professor of Cardiovascular Research, Director, Cardiovascular Research Institute, Harrington Heart and Vascular Institute, University Hospitals, and School of Medicine, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH 44106, United States.
• Santosh Kumar Sirasapalli
  Herman K Hellerstein Professor of Cardiovascular Research, Director, Cardiovascular Research Institute, Harrington Heart and Vascular Institute, University Hospitals, and School of Medicine, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH 44106, United States.
• Mohamed He Makhlouf
  Herman K Hellerstein Professor of Cardiovascular Research, Director, Cardiovascular Research Institute, Harrington Heart and Vascular Institute, University Hospitals, and School of Medicine, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH 44106, United States.
• Sanjay Rajagopalan
  Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
• Sadeer Al-Kindi
  Division of Cardiology, Department of Medicine (S.A.-K., F.N., G.F.A., M.A.C., D.I.S., M.S.A., J.A.M., S.G.T.), Case Western Reserve University, Harrington Heart and Vascular Institute, and University Hospitals Cleveland Medical Center, OH.

Keywords

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