Data Science and Predictive Analytics Biomedical and Health Applications using R /

• Main Author: Dinov, Ivo D. (Author, http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Subjects: Big data.; Health informatics.; Mathematical statistics.; Data mining.; Big Data.; Big Data/Analytics.; Health Informatics.; Probability and Statistics in Computer Science.; Data Mining and Knowledge Discovery.

Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisou...

Table of Contents:

• 1 Introduction
• 2 Foundations of R
• 3 Managing Data in R
• 4 Data Visualization
• 5 Linear Algebra & Matrix Computing
• 6 Dimensionality Reduction
• 7 Lazy Learning: Classification Using Nearest Neighbors
• 8 Probabilistic Learning: Classification Using Naive Bayes
• 9 Decision Tree Divide and Conquer Classification
• 10 Forecasting Numeric Data Using Regression Models
• 11 Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines
• 12 Apriori Association Rules Learning
• 13 k-Means Clustering
• 14 Model Performance Assessment
• 15 Improving Model Performance
• 16 Specialized Machine Learning Topics
• 17 Variable/Feature Selection
• 18 Regularized Linear Modeling and Controlled Variable Selection
• 19 Big Longitudinal Data Analysis
• 20 Natural Language Processing/Text Mining
• 21 Prediction and Internal Statistical Cross Validation
• 22 Function Optimization
• 23 Deep Learning Neural Networks
• 24 Summary
• 25 Glossary
• 26 Index
• 27 Errata.