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

TL;DR

This paper introduces a multi-objective deep learning approach for stroke risk assessment in Shanxi, combining a novel quadratic interactive neural network with explainability tools to improve prediction accuracy and interpretability.

Contribution

It proposes the QIDNN model with quadratic features and a multi-objective optimization framework for better stroke risk prediction and explanation, addressing class imbalance and urgent state recall.

Findings

QIDNN achieves 83.25% accuracy with 7 features.

Recall for attack state improved by 24.9%.

Top features identified include blood pressure and cholesterol.

Abstract

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.