Data Analytics in Biomedical Engineering and Healthcare

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks.

Examines the development and application of data analytics applications in biomedical data Presents innovative classification and regression models for predicting various diseases Discusses genome structure prediction using predictive modeling Shows readers how to develop clinical decision support systems Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks

1. Data analytics applications in biomedical data 2. Predictive Health Analysis 3. Exploration of EHR (Electronic Health Records) using data science 4. Machine Learning and Deep Learning application on medical image analysis 5. Developing Clinical Decision Support System 6. Innovative Classification, Regression Model for predicting various diseases 7. Computational Drug Discovery using State of the Art Unsupervised learning 8. Genome Structure prediction using Predictive modelling 9. Hybrid learning for better medical diagnosis 10. Big data application in healthcare under MapReduce and Hadoop frameworks