Journal of Quality Engineering and Management

Comparing classical statistical and machine learning models in survival analysis

Articles in Press

Document Type : Original Article

Authors

Kimia Samavati 1
Akram Kohansal 1
Hashem Talamkhani 1
Zahra Barikbin 2

1 Department of Statistics, Imam Khomeini International University, Qazvin, Iran

2 Department of Applied Mathematics, Imam Khomeini International University

10.48313/jqem.2026.573578.1599
Abstract
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.

Keywords

Survival Analysis
Heart Failure (HF)
Random Forest
Logistic Regression
Survival Neural Network (DeepSurv)
Subjects
Journal of Quality Engineering and Management

Articles in Press, Accepted Manuscript
Available Online from 05 October 2026

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