Verification and comparison of three prediction models of ischemic stroke in young adults based on the back propagation neural networks

This work aims to explore risk factors for ischemic stroke in young adults and analyze the Traditional Vascular Risk Factors Model based on age, hypertension, diabetes, smoking history, and drinking history. Further, the Lipid Metabolism Model was analyzed based on lipoprotein a [LP (a)], high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein AI (apo AI), apolipoprotein B (apo B), and the Early Renal Injury Model based on urinary microalbuminuria/creatinine ratio (UACR). Besides, we estimated glomerular filtration rate (eGFR), cystatin C (Cys-C), homocysteine (Hcy), β2 microglobulin (β2m), and validated their predictive efficacy and clinical value for the development of ischemic stroke in young adults.We selected and retrospectively analyzed the clinical data of 565 young inpatients admitted to Zhejiang Provincial Hospital of Chinese Medicine between 2010 and 2020, 187 of whom were young stroke patients. A single-factor analysis was used to analyze the risk factors for stroke in young people and developed a traditional vascular risk factors model, a lipid metabolism model, and an early kidney injury model based on backpropagation (BP) neural networks technology to predict early stroke occurrence. Moreover, the prediction performance by the area under the receiver operating characteristics (ROC) curve (AUC) was assessed to further understand the risk factors for stroke in young people and apply their predictive role in the clinical setting.Single-factor analysis showed that ischemic stroke in young adults was associated with hypertension, diabetes, smoking history, drinking history, LP(a), HDL, LDL, apo AI, apo B, eGFR, Cys-C, and β2m (P