نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه آمار، دانشگاه بین المللی امام خمینی (ره)
2 گروه ریاضی کاربردی، دانشگاه بین المللی امام خمینی (ره)
کلیدواژهها
عنوان مقاله English
نویسندگان English
The purpose of this paper is to evaluate the performance of classical survival analysis techniques and contemporary machine learning algorithms in predicting survival outcomes among patients diagnosed with heart failure. The study aims to determine whether machine learning–based models provide improved predictive accuracy compared to conventional statistical approaches. The study applied Kaplan–Meier, log-rank, Cox, and Weibull models alongside machine learning techniques including decision tree, random forest, IPCW logistic regression, SVM, and a survival neural network. Model performances were evaluated based on predictive accuracy. The Cox model performed best among classical methods, while IPCW logistic regression and random forest showed the highest accuracy among machine learning models. The survival neural network performed similarly to Cox and random forest without significant improvement. The paper provides a concise comparison showing that machine learning models can outperform traditional survival analysis approaches in predicting heart failure outcomes.
کلیدواژهها English
