Big Data Analytics System For Predominant Chronic Diseases

Abstract

Medical data is one of the most rewarding and yet most complicated data to analyze. Medical images, biomedical signals and handwritten prescriptions are available and can be used for prediagnostic tasks on the existence of chronic disease by assuming big data analytic concepts. Hence, the main objective of this study was to design a big data analytics prototype that process and visualize the huge amount of dataset by using R-studio programming software. Big data processing and visualization is a challenge that needs new way of tackling which otherwise cannot be solved with current practice of data management because data deluge and data creation frequency in varieties of formats are inevitable scenarios. A big data analytics system that descriptive the occurrence of chronic disease from the big medical data was developed by using different methods and tools. In this study data computation techniques is applied and descriptive analysis were employed. The major new data management techniques are applied to ensure the quality of data and integrate data from different sources. Experimental research design was employed for this study. In addition, (descriptive) analysis approach based on a logistic activation function is employed to build the model. This study achieved as it is possible to manage big data regardless of size and nature of data. The major challenge faced during conducting this study is dealing with heterogeneous data in order to generate insights for improved health-care outcomes or visualization of data. The other most challenging task was the fact that data preserved in Jimma Medical center are disorganized and distributed since it comes from various sources and having different structures and forms. The researcher strongly recommend that prototype with the capability of analyzing and visualizing heterogeneous big data should be developed. As new area of study, it is strongly recommended further studies in specific contexts.