Predicting Risk of Stroke From Lab Tests Using Machine Learning Algorithms: Development and Evaluation of Prediction Models

Abstract

Background Stroke, a cerebrovascular disease, is one of the major causes of death. It causes significant health and financial burdens for both patients and health care systems. One of the important risk factors for stroke is health-related behavior, which is becoming an increasingly important focus of prevention. Many machine learning models have been built to predict the risk of stroke or to automatically diagnose stroke, using predictors such as lifestyle factors or radiological imaging. However, there have been no models built using data from lab tests. Objective The aim of this study was to apply computational methods using machine learning techniques to predict stroke from lab test data. Methods We used the National Health and Nutrition Examination Survey data sets with three different data selection methods (ie, without data resampling, with data imputation, and with data resampling) to develop predictive models. We used four machine learning classifiers and six performance measures to evaluate the performance of the models. Results We found that accurate and sensitive machine learning models can be created to predict stroke from lab test data. Our results show that the data resampling approach performed the best compared to the other two data selection techniques. Prediction with the random forest algorithm, which was the best algorithm tested, achieved an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of 0.96, 0.97, 0.96, 0.75, 0.99, and 0.97, respectively, when all of the attributes were used. Conclusions The predictive model, built using data from lab tests, was easy to use and had high accuracy. In future studies, we aim to use data that reflect different types of stroke and to explore the data to build a prediction model for each type.

Electronic Medical Record Data Analysis and Prediction of Stroke Disease Using Explainable Artificial Intelligence (XAI)

“…Disrupting blood supply to the blood-brain can cause a stroke [13], [41]. This condition causes certain areas of the brain not to receive oxygen and nutrients, resulting in the death of brain cells [14].…”

“…As a result, the body parts controlled by these brain areas cannot function properly [2]. The causes of stroke are generally divided become two, namely the presence of a blood clot in a blood vessel in the brain and a rupture of a blood vessel in the brain [13]. Narrowing or rupture of these blood vessels can occur due to several factors, such as high blood pressure, use of blood-thinning drugs, brain aneurysms, and brain trauma [2].…”

“…Stroke is a disease that arises because of damage to brain blood vessels, so the blood supply that carries oxygen to the brain is disrupted [13]. Depriving the brain of oxygen will cause its cells to die, and the body parts controlled by these brain cells will become dysfunctional [14].…”

Classifier Comparison for Stroke Prediction Ensembling SMOTE+ENN using Machine Learning Approach

“…Alanazi EM et al [7] in their studies used different data selection methods and developed a predictive model using machine learning clasi ers.They performed analysis with data imputation, without data resampling and with data resampling. The predictive model with random forest algorithm produced high results with accuracy of 0.96, sensitivity of 0.97 and speci city of 0.96.…”

An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data

“…Furthermore, Alanazi et al worked on the task of predicting the risk of stroke on an imbalanced clinical dataset (biomarkers) from the National Health and Nutrition Examination Survey (NHANES). Four ML classifiers were tested, and the optimal accuracy (96%) was finally achieved by the Random Forest (RF) algorithm [ 20 ]. Moreover, Cui et al proposed an ML-based model for predicting the incidence and severity of acute ischemic stroke in patients with anterior circulation large vessel occlusion [ 21 ].…”