A Patient Oriented Framework using Big Data & C-means Clustering for Biomedical Engineering Applications
‚??Big data and Machine Learning have changed the healthcare research in recent years. Data generated from Electronic Health Records (EHRs) and other clinical sources now can be used further to help the patients. By applying Big Data Analytics (BDA) into healthcare data, it is possible to predict the outcome or the effects of drugs or risk of developing disease on human body. Several machine learning algorithms such as clustering, classiÔ¨?cation are used to analyze healthcare data. In this article, a framework is proposed using C-means Clustering for Biomedical Engineering applications. The framework can be used to help both the clinicians and the patients. For example, usingthisframework,acliniciancanmakeadecisiontoprescribe suitable drug to a particular patient. In order to develop this framework, data has been collected from UCI machine learning repository. The data then analyzed using a well known big data framework Hadoop.
Big Data, Machine Learning, Hadoop, C-means, ID3, MapReduce