Machine learning is an effective method to predict the 3-month prognosis of patients with acute ischemic stroke

• ¹School of Public Health, Bengbu Medical University, Bengbu, Anhui, China
• ²Department of Neurology, The Second Affiliated Hospital, Bengbu Medical University, Anhui, China
• ³School of Medical Imaging, Bengbu Medical University, Anhui, China
• ⁴Department of Emergency Medicine, The Second Affiliated Hospital, Bengbu Medical University, Anhui, China

Background and objectives: Upwards of 50% of
acute ischemic stroke (AIS) survivors endure varying degrees of
disability, with a recurrence rate of 17.7%. Thus, the prediction of
outcomes in AIS may be useful for treatment decisions. This study aimed
to determine the applicability of a machine learning approach for
forecasting early outcomes in AIS patients.

Methods: A total of 659 patients with new-onset
AIS admitted to the Department of Neurology of both the First and Second
Affiliated Hospitals of Bengbu Medical University from January 2020 to
October 2022 included in the study. The patient’ demographic
information, medical history, Trial of Org 10,172 in Acute Stroke
Treatment (TOAST), National Institute of Health Stroke Scale (NIHSS) and
laboratory indicators at 24 h of admission data were collected. The
Modified Rankine Scale (mRS) was used to assess the 3-mouth outcome of
participants’ prognosis. We constructed nine machine learning models
based on 18 parameters and compared their accuracies for outcome
variables.

Results: Feature selection through the Least
Absolute Shrinkage and Selection Operator cross-validation (Lasso CV)
method identified the most critical predictors for early prognosis in
AIS patients as white blood cell (WBC), homocysteine (HCY), D-Dimer,
baseline NIHSS, fibrinogen degradation product (FDP), and glucose (GLU).
Among the nine machine learning models evaluated, the Random Forest
model exhibited superior performance in the test set, achieving an Area
Under the Curve (AUC) of 0.852, an accuracy rate of 0.818, a sensitivity
of 0.654, a specificity of 0.945, and a recall rate of 0.900.

Conclusion: These findings indicate that RF models
utilizing general clinical and laboratory data from the initial 24 h of
admission can effectively predict the early prognosis of AIS patients.(So predicting failure to recover? How does that help survivors?)

More at link.