An Effort Estimation Model for Agile Software Development Using Machine Learning In Healthcare

Abstract

Most of the software projects are not successful because of their wrong cost estimation. To palliate the wrong estimation, the accurate and effective estimation in all the software development process is required. Estimation has significant role in software development as it estimates its cost and the effort required thus resulting in overall success of software development. So far many effort estimation models have been developed for software projects, most among them produced accurate result, but did not provide flexibility during decision making of software development process. Choose the right method and right practices and applying them adequately in effort estimation of the software predicts the success of software development. The main objective of this paper is to estimate effort in healthcare while using the scrum in agile machine learning. The rising cost of Health care is one of the concerns for poor people around the world. Agile system in health care can provide an efficient framework for streamlining and improving governance. To Estimate the cost of healthcare the model is developed using machine learning algorithms. Story point as a unit of measure is used to assess the efforts involved in an issue. The generated model was tested using Precision, AUC, Recall, and Accuracy. The results of the estimate were then compared to story point estimation. The purpose of this paper is to estimate the size and effort in agile development from story points, which are user stories, are calculated using user learning techniques to improve the efficiency and accuracy of healthcare in various ways.