Implementation and Analysis of Predictive Algorithms for Healthcare Data in Cloud Environment

In the present era of massive usage of computers, a large set of data is being generated from different organizations each day, each hour and each second. This data would be of prodigious use to a diverse set of people based on their needs. Predictive analysis is a process of analyzing and identifying the different patterns to predict the occurrence of these patterns in the future. The expected output can help to plan a new strategy and adopt innovative solutions for decision making. In the proposed system, predictive models of supervised learning are applied to the Healthcare Data. The models which are analyzed will be available on the cloud environment for future action. It can be accessed by those concerned for potential analysis. The work focuses on two cases (1) Analysis of the algorithms on the healthcare domain and (2) Hosting the models to the cloud. The empirical analysis considers supervised algorithms such as Naive Bayes, decision trees, random forest, and linear regression. The predictive algorithms are then tested and analyzed for performance metrics such as accuracy, precision, f1-score, and recall. Based on these performance metrics, results in the Random forest algorithm showed better outcomes for the Healthcare dataset.