A General Machine Learning Framework for Predicting the Survival of ۱۵ Years Patients with Brain Stroke
سال انتشار: 1403
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 37
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شناسه ملی سند علمی:
DSAI01_039
تاریخ نمایه سازی: 4 تیر 1403
چکیده مقاله:
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 ۲۰۰۸ to ۲۰۲۳. 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 ۱۵ years. With a high accuracy (۸۶.۳%) and substantial AUC of ۹۱% (۹۵% CI ۰.۸۳ - ۰.۹۸), this model is reliable for long-term survival analysis. The identification of common risk factors such as age, sex, cerebrovascular accident type (ischemic), history of cerebrovascular accident (yes), job, and physical activity. 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.
کلیدواژه ها:
نویسندگان
Solmaz Norouzi
Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
Mohammad Asghari Jafarabadi
Cabrini Research, Cabrini Health, VIC ۳۱۴۴, Australia
Ebrahim Hajizadeh
School of Public Health and Preventative Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, VIC ۳۰۰۴, Melbourne Australia