Multi-objective learning and explanation for stroke risk assessment in Shanxi province.

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Stroke is the leading cause of death in China (Zhou et al. in The Lancet, 2019). A dataset from Shanxi Province is analyzed to predict the risk of patients at four states (low/medium/high/attack) and to estimate transition probabilities between various states via a SHAP DeepExplainer. To handle the issues related to an imbalanced sample set, the quadratic interactive deep model (QIDeep) was first proposed by flexible selection and appending of quadratic interactive features. The experimental results showed that the QIDeep model with 3 interactive features achieved the state-of-the-art accuracy 83.33%(95% CI (83.14%; 83.52%)). Blood pressure, physical inactivity, smoking, weight, and total cholesterol are the top five most important features. For the sake of high recall in the attack state, stroke occurrence prediction is considered an auxiliary objective in multi-objective learning. The prediction accuracy was improved, while the recall of the attack state was increased by 17.79% (to 82.06%) compared to QIDeep (from 71.49%) with the same features. The prediction model and analysis tool in this paper provided not only a prediction method but also an attribution explanation of the risk states and transition direction of each patient, a valuable tool for doctors to analyze and diagnose the disease.





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Ma, Jing, Yiyang Sun, Junjie Liu, Huaxiong Huang, Xiaoshuang Zhou and Shixin Xu (2022). Multi-objective learning and explanation for stroke risk assessment in Shanxi province. Scientific reports, 12(1). p. 22337. 10.1038/s41598-022-26595-z Retrieved from

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Shixin Xu

Assistant Professor of Mathematics at Duke Kunshan University

Shixin Xu is an Assistant Professor of Mathematics.   His research interests are machine learning and data-driven models for diseases,  multiscale modeling of complex fluids, Neurovascular coupling, homogenization theory, and numerical analysis.  The current projects he is working on are

  • image data-based for the prediction of hemorrhagic transformation in acute ischemic stroke,
  • electrodynamics modeling of saltatory conduction along a myelinated axon
  • electrochemical modeling
  • fluid-structure interaction with mass transportation and reaction

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