Advancements in Predictive Modeling for Biomedical Data Mining and Analysis

By Sudipta Roy, a renowned author in the field of machine learning, “Predictive Modeling in Biomedical Data Mining and Analysis” presents groundbreaking research findings and technological advancements in the field of machine learning in biomedical image and data analysis. The book delves into recent technologies and studies in preclinical and clinical practice in computational intelligence, offering insights into the science of processing, analyzing, and utilizing all aspects of advanced computational machine learning in biomedical image and data analysis.

As the application of machine learning continues to spread to a variety of biomedical problems, including automatic image segmentation, image classification, disease classification, fundamental biological processes, and treatments, this book serves as an ideal reference for researchers, students, and professionals in the field of biomedical engineering.

Machine Learning Techniques in Biomedical Applications

Machine learning techniques are increasingly being used as predictive models for a wide range of applications, including biomedical applications. These techniques have shown impressive results across a variety of domains 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.

Supervised and Unsupervised Learning for Medical Diagnosis

The book includes predictive modeling algorithms for both supervised learning and unsupervised learning, which are crucial for medical diagnosis, data summarization, and pattern identification. These algorithms enable researchers and professionals to analyze complex biomedical data effectively and make accurate predictions.

Data Visualization and Information Retrieval

“Predictive Modeling in Biomedical Data Mining and Analysis” offers comprehensive 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. These topics are essential for understanding how to process and analyze large datasets in biomedical research.

High Dimension Data and Data Reduction

The book also covers the processing of 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. These topics are critical for researchers and professionals seeking to improve the efficiency and accuracy of their data analysis methods.

Overall, “Predictive Modeling in Biomedical Data Mining and Analysis” is a valuable resource for anyone interested in the latest advancements in machine learning and its applications in biomedical engineering. The book provides a comprehensive overview of the current state of the field and offers insights into future directions for research and development.