Background: Outcomes of stroke with cancer-related coagulopathy (Trousseau syndrome) is predominantly attributed to cancer managements; however, stroke management by anticoagulants can contribute to the best supportive care. We aimed to find predictors of the outcome by multivariate analysis, including machine-learning (ML) based feature-engineering. Methods: A single-center retrospective study using a prospective cohort was conducted between April 2011 and June 2019. Out of the cumulative total of 110 acute ischemic stroke patients with malignancy, 65 were treated with anticoagulants, including warfarin (n=19), non-vitamin K dependent oral anticoagulants (NOAC, n=40), or subcutaneous heparin injections (n=6). Cancer-related coagulopathy was defined by elevated blood D-dimer levels at the onset of stroke with malignancy. The incidence of stroke recurrence was analyzed using 40 variables by logistic regression (LR) and in-house ML programs. Results: Out of 65 instances of the cancer-related stroke, 12 (18.5%) stroke recurrences were observed during 455 ± 70 days (mean, SEM). The stroke subtypes were cardioembolism (n=2), stroke with undetermined etiology (n=23) or other determined etiology (cancer-related coagulopathy, n=40). Multivariate LR revealed significant predictors of stroke recurrence, including NOAC usage and stroke subtype. Whereas, combination of forward stepwise selection and Naïve-Bayes (NB) or support vector machine found the blood D-dimer level as an additional important predictor. Input the D-dimer level in addition to NOAC usage and stroke subtype yielded the best area under the curve (AUC) for either of LR or NB compared to input warfarin or heparin usage. AUC for the LR for these 3 variables was better than that for NB. Conclusion: This study suggests the incidence of stroke recurrence is high in this clinical situation. NOAC usage, stroke subtype, and blood D-dimer level at the onset of stroke have predictive value of the outcome.