Im Jahr 2020 wurde die Inzidenz neuer Krebsfälle weltweit auf 19,1 Millionen und fast 10 Millionen Krebstote geschätzt [1]. Die positiven Wirkungen von Bewegung bei Krebspatienten sind in der klinischen Praxis gut belegt. Vor Beginn eines überwachten Trainingsprogramms im klinischen Umfeld müssen sich die Patienten einer körperlichen Untersuchung unterziehen. Der Fahrradergometrie-Test stellt dabei einen integralen Bestandteil der körperlichen Untersuchung dar, da er dem Therapeuten bzw. der Therapeutin oder Arzt bzw. Ärztin die wesentlichen Informationen über die körperliche Leistungsfähigkeit eines Patienten liefert. Nichtsdestotrotz erfordert der Fahrradergometrie-Test viel Zeit, Personal, Ausrüstung und Raum. Darüber hinaus besteht aufgrund einiger Risiken belastungsinduzierter unerwünschter Ereignisse ein natürlicher Bedarf an unterschiedlichen Ansätzen, um ebendiese Risiken zu minimieren. Daher zielte diese Arbeit darauf ab, die Anthropometrie, das Alter und das Geschlecht zu nutzen, um die Spitzenwattzahl des Fahrradergometrietests und das Fitnessleistungsniveau bei Krebspatient*innen unter Verwendung verschiedener Techniken des maschinellen Lernens (ML) vorherzusagen.Methoden: Die vorliegenden Daten wurden von 1712 Probanden erhoben, die im Zeitraum Juli/2016 bis Juni/2019 in die stationäre Rehabilitation eintraten. Alle Proband*innen haben den Fahrradergometrie-Test absolviert, der ein übliches Verfahren vor der Anmeldung zur Sporttherapie ist. Nach einem Fahrradergometrie-Test wurden die Proband*innen entsprechend ihrem Testergebnis in drei Fitness Performance Level (FPL) eingeteilt: schlecht, mittel oder gut. Lineare Regressionen, Support-Vektor-Regressoren und Random-Forest-Regressor Modelle wurden dann trainiert, um kontinuierliche Ergebnisvariablen (Spitzenwatt) vorherzusagen, wobei logistische Regressionen, Support-Vektor-Klassifikatoren, Random-Forest-Klassifikatoren und naive Bayes verwendet wurden, um die kategorische FPL-Variable vorherzusagen. Di

The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on: Foundations of Imbalanced Learning Imbalanced Datasets: From Sampling to Classifiers Ensemble Methods for Class Imbalance Learning Class Imbalance Learning Methods for Support Vector Machines Class Imbalance and Active Learning Nonstationary Stream Data Learning with Imbalanced Class Distribution Assessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.

Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective explores various cancers using Artificial Intelligence techniques. It presents the rapid advancement in the existing prediction models by applying Machine Learning techniques. Several applications of Machine Learning in different cancer prediction and treatment options are discussed, including specific ideas, tools and practices most applicable to product/service development and innovation opportunities. The wide variety of topics covered offers readers multiple perspectives on various disciplines. Features • Covers the fundamentals, history, reality and challenges of cancer • Presents concepts and analysis of different cancers in humans • Discusses Machine Learning-based deep learning and data mining concepts in the prediction of cancer • Offers real-world examples of cancer prediction • Reviews strategies and tools used in cancer prediction • Explores the future prospects in cancer prediction and treatment Readers will learn the fundamental concepts and analysis of cancer prediction and treatment, including how to apply emerging technologies such as Machine Learning into practice to tackle challenges in domains/fields of cancer with real-world scenarios. Hands-on chapters contributed by academicians and other professionals from reputed organizations provide and describe frameworks, applications, best practices and case studies on emerging cancer treatment and predictions. This book will be a vital resource to graduate students, data scientists, Machine Learning researchers, medical professionals and analytics managers.

The IEEE ICECCT 2019 aims to offer a great opportunity to bring together professors, researchers and scholars around the globe a great platform to deliver the latest innovative research results and the most recent developments and trends in Electrical, Electronics and Computer Engineering and Technology fields The conference will feature invited talks from eminent personalities all around the world, pre conference tutorial workshops and referred paper presentations The vision of IEEE ICECCT 2019 is to promote foster communication among researchers and practitioners working in a wide variety of the above areas in Engineering and Technology

This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.

Lung cancer is a major cause of cancer-related deaths worldwide. Machine learning techniques have shown promising results in the early detection and prediction of lung cancer. However, high-dimensional data, such as gene expression profiles, can introduce noise and decrease the classification accuracy of machine learning models. Feature selection techniques can alleviate this issue by identifying the most relevant and informative features, leading to better model performance. Optimized feature selection techniques can enhance the prediction accuracy of lung cancer using machine learning algorithms. Support vector machines, random forest, and artificial neural networks are commonly used algorithms for lung cancer prediction. By optimizing feature selection, these models can be trained with the most informative features, reducing overfitting and improving classification accuracy. Cross-validation techniques can also be used to evaluate the performance of feature selection and machine learning algorithms. The integration of optimized feature selection with machine learning techniques can provide an accurate and reliable lung cancer prediction model, which has the potential to improve early detection and precision medicine for lung cancer patients. Overall, optimized feature selection for enhancing lung cancer prediction using machine learning techniques is a promising approach to improving patient outcomes and reducing the global burden of lung cancer.