{Reference Type}: Journal Article
{Title}: Machine Learning-based Cerebral Venous Thrombosis Diagnosis with Clinical Data.
{Author}: Namjoo-Moghadam A;Abedi V;Avula V;Ashjazadeh N;Hooshmandi E;Abedinpour N;Rahimian Z;Borhani-Haghighi A;Zand R;
{Journal}: J Stroke Cerebrovasc Dis
{Volume}: 0
{Issue}: 0
{Year}: 2024 Jul 2
{Factor}: 2.677
{DOI}: 10.1016/j.jstrokecerebrovasdis.2024.107848
{Abstract}: <B>OBJECTIVE: </B>Cerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT relies on early diagnosis. Our study focuses on developing a machine learning-based screening algorithm using clinical data from a large neurology referral center in southern Iran.<BR><B>METHODS: </B>The Iran Cerebral Venous Thrombosis Registry (ICVTR code: 9001013381) provided data on 382 CVT cases from Namazi Hospital. The control group comprised of adult headache patients without CVT as confirmed by neuroimaging and was retrospectively selected from those admitted to the same hospital. We collected 60 clinical and demographic features for model development and validation. Our modeling pipeline involved imputing missing values and evaluating four machine learning algorithms: generalized linear model, random forest, support vector machine, and extreme gradient boosting.<BR><B>RESULTS: </B>A total of 314 CVT cases and 575 controls were included. The highest AUROC was reached when imputation was used to estimate missing values for all the variables, combined with the support vector machine model (AUROC=0.910, Recall=0.73, Precision=0.88). The best recall was achieved also by the support vector machine model when only variables with less than 50% missing rate were included (AUROC=0.887, Recall=0.77, Precision=0.86). The random forest model yielded the best precision by using variables with less than 50% missing rate (AUROC=0.882, Recall=0.61, Precision=0.94).<BR><B>CONCLUSIONS: </B>The application of machine learning techniques using clinical data showed promising results in accurately diagnosing CVT within our study population. This approach offers a valuable complementary assistive tool or an alternative to resource-intensive imaging methods.