Computational Learning Approaches to Data Analytics in Biomedical Applications

Distinguishes theories and applications in computational learning as they relate to translational medicine

Khalid Al-Jabery (Author), Tayo Obafemi-Ajayi (Author), Gayla Olbricht (Author), Donald Wunsch (Author)

9780128144824, Elsevier Science

Hardback, published 20 November 2019

310 pages
23.4 x 19 x 2.3 cm, 0.79 kg

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained.

1. Introduction2. Data Preparation3. Clustering Algorithms4. Supervised learning5. Statistical Analysis tools and techniques6. Genomic Data Analysis7. Evaluation Metrics8. Visualization9. Bio informatics tools in MATLAB and Python

Subject Areas: Biomedical engineering [MQW]