a general machine learning framework for predicting the survival of 15 years patients with brain stroke

The objective of our study was to compare different machine learning and cox models for accurately predicting mortality and survival in brain stroke patients. brain stroke is known as one of the main causes of death worldwide. additionally, we sought to identify the key variables that contribute to the precise prediction and classification of patients. to achieve this objective, we conducted a study using machine learning techniques and cox on data from ardabil, iran, spanning from 2008 to 2023. survival analysis, which involves modeling time-to-event data, was employed in our study. seven algorithms were trained using r software, and the best model was chosen for further analysis based on its diagnostic performance. k‒m survival probabilities were calculated, and log-rank tests were conducted. the results of this study demonstrate the effectiveness of ml models, particularly the lr model, in comparison to the cox model in accurately predicting mortality and survival in brain stroke patients over extended periods of 15 years. with a high accuracy (86.3%) and substantial auc of 91% (95% ci 0.83 - 0.98), this model is reliable for long-term survival analysis. the identification of common risk factors such as age, sex, cvtype, cvhis, job, and phactivity provides valuable insights for clinicians in risk assessment. these findings contribute to the advancement of personalized care strategies and highlight the potential of ml in enhancing prognostic precision for brain stroke patients.