Prediction of neurologic deterioration based on support vector machine algorithms and serum osmolarity equations

Abstract

ObjectiveDehydration on admission is correlated with neurological deterioration (ND). The primary objective of our study was to use support vector machine (SVM) algorithms to identify an ND prognostic model, based on dehydration equations.MethodsThis study included a total of 382 patients hospitalized with acute ischemic stroke. The following parameters were recorded: age, sex, laboratory values (serum sodium, potassium, chlorinum, glucose, and urea), and vascular risk factor data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative performance of the BUN/Cr ratio as well as each of 38 equations for predicting ND. We used the Boruta algorithm for feature selection. After optimizing the SVM kernel parameters, we built an SVM model to predict ND and used the test set to obtain predictive values for assessing model accuracy.ResultsIn total, 102 of 382 patients (26.7%) with acute ischemic stroke developed ND. In all patients, the BUN/Cr ratio and each of 38 equations were significant predictors of ND. Equation 20 [1.86 × Na+ + glucose + urea + 9] yielded the maximum area under the ROC curve, and faired best in terms of prognostic performance (a cutoff value of 284.49 mM yielded a sensitivity of 94.12% and specificity of 61.43%). Equation 32 predicted ND poststroke across population groups, and worked well in older as well as young adults; (a cutoff value of 297.08 mM yielded a sensitivity of 93.14% and specificity of 60.00%). Feature selection by the Boruta algorithm was used to decrease the number of variables from 18 to 5 in the condition. The specificity of test samples for the SVM prediction model increased from 44.1% to 89.4%, and the AUC increased from 0.700 to 0.927.Conclusions SVM algorithms can be used to establish a prediction model for dehydration‐associated ND, with good classification results.

A systematic review of machine learning models for predicting outcomes of stroke with structured data

“…Almost all studies (17) were published as peer reviewed publications in biostatistical or clinical journals. All included studies were published after 2007, with almost half (8) published after 2016, and 3 studies were published in 2018 [11][12][13] and 2019 [14][15][16] each (Fig 2). In terms of regions under study, UK (3) [17][18][19], Germany (2) [20,21], Turkey (2) [22,23] and China (2) [13,14] make up half of the sample.…”

“…All included studies were published after 2007, with almost half (8) published after 2016, and 3 studies were published in 2018 [11][12][13] and 2019 [14][15][16] each (Fig 2). In terms of regions under study, UK (3) [17][18][19], Germany (2) [20,21], Turkey (2) [22,23] and China (2) [13,14] make up half of the sample. Saudi Arabia [24], Australia [25], Korea [15], USA [26], Denmark [27], Netherlands [11], Portugal [12], Taiwan [28] and Japan [16] had one study each.…”

“…Single centre studies (10) were slightly more common than multi-centre studies (8). For sources of data, half of the studies (9) used registry data while the rest used EHR (4) [13,22,24,28], cohort (3) [14,15,27], and clinical trial data (2) [11,18]. All the included studies focused on developing new models using ML whilst no study validated existing ML based predictive models on independent data.…”

“…length of stay [18] used DT and Bayesian Network, post-stroke spasticity [25] used RF, DT, and Adaboost, neurologic deterioration [13] used SVM, quality of life [23] used ANFIS, and delayed cerebral ischemia after aneurysmal subarachnoid haemorrhage (SAH) [16] used RF were also predicted as stroke outcomes. Among the included studies, ten studies reported having missing values, one study [28] reported no missing values, and seven studies did not mention missing values.…”

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

“…These two algorithms will select different sets of variables, thereby reducing the likelihood of important variables being omitted. Boruta and LASSO have been used for variable selection for various types of data, such as survey, medical and genomic data [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ].…”