Advancements in Predictive Modeling for Biomedical Data Mining and Analysis

Author: Sudipta Roy

Publication Date: August 28, 2022

ISBN: 9780323998642, 9780323914451

Language: English

Publisher: Elsevier Science

Overview

Predictive Modeling in Biomedical Data Mining and Analysis presents significant technical advancements and research findings in the field of machine learning in biomedical image and data analysis. The book examines recent technologies and studies in preclinical and clinical practice in computational intelligence. The authors present leading-edge research in the science of processing, analyzing, and utilizing all aspects of advanced computational machine learning in biomedical image and data analysis.

Key Features

Machine Learning Techniques: The book discusses machine learning techniques used as predictive models for various biomedical applications, including automatic image segmentation, image classification, disease classification, fundamental biological processes, and treatments.

Supervised and Unsupervised Learning: It includes predictive modeling algorithms for both supervised learning and unsupervised learning for medical diagnosis, data summarization, and pattern identification.

Biomedical Data Processing: The book offers complete coverage of predictive modeling in biomedical applications, including data visualization, information retrieval, data mining, image pre-processing and segmentation, mathematical models, and deep neural networks.

High Dimension Data: It provides readers with leading-edge coverage of biomedical data processing, including high dimension data, data reduction, clinical decision-making, deep machine learning in large data sets, multimodal, multi-task, and transfer learning, as well as machine learning with Internet of Biomedical Things applications.

Why This Book?

Predictive Modeling in Biomedical Data Mining and Analysis is an ideal reference for those interested in the application of machine learning in biomedical engineering research. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood, hence the need for new resources and information.