Medical Software Engineering Cost Estimation Model Based on Multimodal Improved Genetic Algorithm

Abstract

Cost estimation is crucial in the development of medical software and is typically achieved through the integration of cost data and the application of AI algorithms. Traditional methods, such as unoptimized machine learning, struggle with inefficiencies and low accuracy in processing large volumes of dynamic data. To address these issues, the present study has developed an enhanced cost estimation model for medical software engineering using an mutimodal improved genetic algorithm. This model aims to enhance responsiveness, adaptability, and accuracy. We employed a COX proportional hazards regression model to analyze factors influencing development costs. Additionally, we integrated an Arithmetic Optimization Algorithm (AOA) with a Dwarf Mongoose Optimizer (DMOA) to identify cost features using AOA-GA and to predict costs using DMOA-GA. The results indicate enhanced sensitivity to data input changes, high accuracy, and stability, effectively facilitating cost estimation in complex environments for medical software projects.