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

Ma, Jing

Sun, Yiyang

Liu, Junjie

Huang, Huaxiong

Zhou, Xiaoshuang

Xu, Shixin

2023-05-27T14:19:35Z

2023-05-27T14:19:35Z

2022-12

2023-05-27T14:19:33Z

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.

10.1038/s41598-022-26595-z

2045-2322

2045-2322

https://hdl.handle.net/10161/27414

eng

Springer Science and Business Media LLC

Scientific reports

10.1038/s41598-022-26595-z

Humans

Risk Assessment

Smoking

Learning

Blood Pressure

Stroke

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

Journal article

Xu, Shixin|0000-0002-8207-7313

22337

1

Duke

Duke Kunshan University

DKU Faculty

Published

12