Post-stroke Anxiety Analysis via Machine Learning Methods.

Jirui Wang,Meiqing Lin,Defeng Zhao,Xiuli Shang,Xinyu Huang

doi:10.3389/fnagi.2021.657937

Abstract

Post-stroke anxiety (PSA) has caused wide public concern in recent years, and the study on risk factors analysis and prediction is still an open issue. With the deepening of the research, machine learning has been widely applied to various scenarios and make great achievements increasingly, which brings new approaches to this field. In this paper, 395 patients with acute ischemic stroke are collected and evaluated by anxiety scales (i.e., HADS-A, HAMA, and SAS), hence the patients are divided into anxiety group and non-anxiety group. Afterward, the results of demographic data and general laboratory examination between the two groups are compared to identify the risk factors with statistical differences accordingly. Then the factors with statistical differences are incorporated into a multivariate logistic regression to obtain risk factors and protective factors of PSA. Statistical analysis shows great differences in gender, age, serious stroke, hypertension, diabetes mellitus, drinking, and HDL-C level between PSA group and non-anxiety group with HADS-A and HAMA evaluation. Meanwhile, as evaluated by SAS scale, gender, serious stroke, hypertension, diabetes mellitus, drinking, and HDL-C level differ in the PSA group and the non-anxiety group. Multivariate logistic regression analysis of HADS-A, HAMA, and SAS scales suggest that hypertension, diabetes mellitus, drinking, high NIHSS score, and low serum HDL-C level are related to PSA. In other words, gender, age, disability, hypertension, diabetes mellitus, HDL-C, and drinking are closely related to anxiety during the acute stage of ischemic stroke. Hypertension, diabetes mellitus, drinking, and disability increased the risk of PSA, and higher serum HDL-C level decreased the risk of PSA. Several machine learning methods are employed to predict PSA according to HADS-A, HAMA, and SAS scores, respectively. The experimental results indicate that random forest outperforms the competitive methods in PSA prediction, which contributes to early intervention for clinical treatment.

Highlights

Stroke is a medical condition in which poor blood flow to the brain results in cell death, associated with high morbidity, high disability, and high mortality across the world (Wolfe, 2000)
With the aid of ensemble learning, random forest can be applied in Post-stroke anxiety (PSA) prediction
We carry out a series of experiments to analyze the risk factors and employ machine learning methods to predict PSA

Summary

Introduction

Stroke is a medical condition in which poor blood flow to the brain results in cell death, associated with high morbidity, high disability, and high mortality across the world (Wolfe, 2000). Approximately 2.5 million new stroke cases annually occur in China and the mortality rate has reached 11.48% (Sun et al, 2013; Chen et al, 2017). Mood problems such as depression, apathy, and distress are commonly reported with post-stroke (Hackett et al, 2014), but anxiety in stroke. A plethora of studies indicate that PSA significantly influences the living quality (Lincoln et al, 2013), which is associated with the delaying recovery of neurological function (Chun et al, 2018), and the interventions on anxiety disorders have a positive impact on the incidence of both coronary artery disease and stroke (Pérez-Piñar et al, 2017)

Methods

Results

Discussion

Conclusion