Data from: Mathematical modeling for the prediction of cerebral white matter lesions based on clinical examination data

Cerebral white matter lesions are ischemic symptoms caused mainly by
microangiopathy; they are diagnosed by MRI because they show up as
abnormalities in MRI images. Because patients with white matter lesions do
not have any symptoms, MRI often detects the lesions for the first time.
Generally, head MRI for the diagnosis and grading of cerebral white matter
lesions is performed as an option during medical checkups in Japan. In
this study, we develop a mathematical model for the prediction of white
matter lesions using data from routine medical evaluations that do not
include a head MRI. Linear discriminant analysis, logistic discrimination,
Naive Bayes classifier, support vector machine, and random forest were
investigated and evaluated by ten-fold cross-validation, using clinical
data for 1,904 examinees (988 males and 916 females) from medical checkups
that did include the head MRI. The logistic regression model was selected
based on a comparison of accuracy and interpretability. The model
variables consisted of age, gender, plaque score (PS), LDL, systolic blood
pressure (SBP), and administration of antihypertensive medication (odds
ratios: 2.99, 1.57, 1.18, 1.06, 1.12, and 1.52, respectively) and showed
Areas Under the ROC Curve (AUC) 0.805, the model displayed sensitivity of
72.0%, and specificity 75.1% when the most appropriate cutoff value was
used, 0.579 as given by the Youden Index. This model has shown to be
useful to identify patients with a high-risk of cerebral white matter
lesions, who can then be diagnosed with a head MRI examination in order to
prevent dementia, cerebral infarction, and stroke.