The machine learning methods to analyze the using strategy of antiplatelet drugs in ischaemic stroke patients with gastrointestinal haemorrhage

BACKGROUND: For ischaemic stroke patients with gastrointestinal haemorrhage, stopping antiplatelet drugs or reducing the dose of antiplatelet drugs was a conventional clinical therapy method. But not a study to prove which way was better. And the machinery learning methods could help to obtain which way more suit for some patients. METHODS: Data from consecutive ischaemic stroke patients with gastrointestinal haemorrhage were prospectively collected. The outcome was a recurrent stroke rate, haemorrhage events, mortality and favourable functional outcome (FFO). We analysed the data using conventional logistic regression methods and a supervised machine learning model. We used unsupervised machine learning to group and analyse data characters. RESULTS: The patients of stopping antiplatelet drugs had a lower rate of bleeding events (p = 0.125), mortality (p = 0.008), rate of recurrence of stroke (p = 0.161) and distribution of severe patients (mRS 3–6) (p = 0.056). For Logistic regression, stopping antiplatelet drugs (OR = 2.826, p = 0.030) was related to lower mortality. The stopping antiplatelet drugs in the supervised machine learning model related to mortality (AUC = 0.95) and FFO (AUC = 0.82). For group by unsupervised machine learning, the patients of better prognosis had more male (p < 0.001), younger (p < 0.001), had lower NIHSS score (p < 0.001); and had a higher value of serum lipid level (p < 0.001). CONCLUSIONS: For ischemic stroke patients with gastrointestinal haemorrhage, stopping antiplatelet drugs had a better prognosis. Patients who were younger, male, with lesser NIHSS scores at admission, with the fewest history of a medical, higher value of diastolic blood pressure, platelet, blood lipid and lower INR could have a better prognosis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12883-023-03422-0.