Data science for healthcare predictive analytics

Abstract

Big data are everywhere nowadays. Many businesses possess big data for their success because big data are very useful and are considered as new oil. For instance, big data are very important in predicting the trends on what will happen in the future. Many researchers have generated or gathered data to further enhance their research and to apply them to numerous real-life applications. Examples of big data include healthcare patient data. To improve the detection of illnesses and diseases, researchers have gathered healthcare patient data, examined the diagnosis on healthcare patient data (e.g., cells, blood count, antibodies count), and compared with previous data to determine if a specific illness or disease exist. Having an automatic predictive method for healthcare and disease analytics would be desirable. In this paper, we focus on healthcare mining, which aims to computationally discover knowledge from healthcare data. In particular, we present a data science framework with two predictive analytic algorithms for accurate prediction on the trends of cancer cases. The algorithms predict cancerous cells based on the information of the cell data from some data samples. Evaluation results on several real-life datasets related to the breast cancer demosntrate the effectiveness of our data science framework and predictive algorithms in healthcare data analytics.