Journal of Quality Engineering and Management

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.

Abstract Purpose: This paper aims to estimate the parameters of the Weibull distribution using a genetic algorithm and compare its performance with traditional estimation methods.
Methodology: A simulation study was conducted under different sample sizes and censoring levels. The genetic algorithm was applied to maximize the likelihood function.
Findings: The results show that the genetic algorithm provides more accurate and stable parameter estimates compared to the maximum likelihood method, especially in the presence of censored data.
Originality/Value: This study presents a novel application of genetic algorithms in reliability analysis, demonstrating their effectiveness in parameter estimation for censored datasets.