An Approach to Cost Estimation in Medical Software Based on Improved Genetic Algorithm

Accurate estimation of medical software development costs is critical for project planning and resource management. Conventional genetic algorithms often show limited efficiency and suboptimal accuracy on large-scale, heterogeneous datasets. To address these issues, the authors propose an enhanced genetic algorithm framework that integrates the AO optimizer with the Chernobyl disaster optimizer (CDO). The AO optimizer supports global search and feature extraction of cost parameters, while the CDO enhances prediction precision and helps avoid local optima. Key cost-related variables were identified and assessed using logistic regression, with results visualized via survival and forest plots. This dual-optimizer design improves convergence speed, adaptability, and predictive reliability. Experiments confirm that the model achieves higher sensitivity and accuracy compared to traditional methods. Overall, the approach provides a robust and scalable tool for medical software cost estimation, highlighting the value of hybrid intelligent optimization in medical informatics.