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

JMIR Form Res

Department of Health Informatics and Administration, College of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, United States.

Published: December 2021

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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8686476PMC
http://dx.doi.org/10.2196/23440DOI Listing

Publication Analysis

Top Keywords

machine learning
20
stroke lab
12
data
12
data resampling
12
stroke
8
risk stroke
8
lab tests
8
learning models
8
models built
8
built data
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!