Multi-objective optimization and explanation for stroke risk assessment in Shanxi province.
2021
Stroke is the top leading causes of death in China (Zhou et al. The Lancet
2019). A dataset from Shanxi Province is used to identify the risk of each
patient's at four states low/medium/high/attack and provide the state
transition tendency through a SHAP DeepExplainer. To improve the accuracy on an
imbalance sample set, the Quadratic Interactive Deep Neural Network (QIDNN)
model is first proposed by flexible selecting and appending of quadratic
interactive features. The experimental results showed that the QIDNN model with
7 interactive features achieve the state-of-art accuracy $83.25\%$. Blood
pressure, physical inactivity, smoking, weight and total cholesterol are the
top five important features. Then, for the sake of high recall on the most
urgent state, attack state, the stroke occurrence prediction is taken as an
auxiliary objective to benefit from multi-objective optimization. The
prediction accuracy was promoted, meanwhile the recall of the attack state was
improved by $24.9\%$ (to $84.83\%$) compared to QIDNN (from $67.93\%$) with
same features. The prediction model and analysis tool in this paper not only
gave the theoretical optimized prediction method, but also provided the
attribution explanation of risk states and transition direction of each
patient, which provided a favorable tool for doctors to analyze and diagnose
the disease.
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