Story Point Based Effort Estimation Model with Machine Learning Techniques in Healthcare

Abstract

Healthcare diseases diagnoses projects often exceed schedules and budgets due to the complication and their large size. Project planning and estimation are exclusively challenging in large and globally distributed projects. Agile with machine learning techniques can overcome these challenges of healthcare projects largely. The purpose of this study is to estimate efforts in large-scale healthcare projects using user story point with machine learning technique. Will be using user stories to estimates the size of Healthcare projects. The templates of user stories: As a [user or destination], I [want to some goal] so that [the goal can be achieved]. According to Bill Wake a good user story must have INVEST acronym. To predict accuracy of machine learning algorithms such as Naïve Bayes, Decision Tree, Logistic Regression cancer patient dataset has used. Writing user stories for healthcare is an easy way to maintain the requirements for an agile project. Agile model in healthcare has used to maintain the effectiveness by adopting the frequently changing demand of the customers. This research paper work on cancer diseases and will provide accuracy, precision, recall using machine learning algorithms such as naïve Bayes, logistic regression, Decision Tree.