Editors: Ilker Ozsahin, Dilber Uzun Ozsahin

Applied Machine Learning and Multi-criteria Decision-making in Healthcare

eBook: US $99 Special Offer (PDF + Printed Copy): US $168
Printed Copy: US $119
Library License: US $396
ISBN: 978-1-68108-872-3 (Print)
ISBN: 978-1-68108-871-6 (Online)
Year of Publication: 2021
DOI: 10.2174/97816810887161210101

Introduction

This book provides an ideal foundation for readers to understand the application of artificial intelligence (AI) and machine learning (ML) techniques to expert systems in the healthcare sector. It starts with an introduction to the topic and presents chapters which progressively explain decision-making theory that helps solve problems which have multiple criteria that can affect the outcome of a decision. Key aspects of the subject such as machine learning in healthcare, prediction techniques, mathematical models and classification of healthcare problems are included along with chapters which delve in to advanced topics on data science (deep-learning, artificial neural networks, etc.) and practical examples (influenza epidemiology and retinoblastoma treatment analysis).

Key Features:

  • - Introduces readers to the basics of AI and ML in expert systems for healthcare
  • - Focuses on a problem solving approach to the topic
  • - Provides information on relevant decision-making theory and data science used in the healthcare industry
  • - Includes practical applications of AI and ML for advanced readers
  • - Includes bibliographic references for further reading

The reference is an accessible source of knowledge on multi-criteria decision-support systems in healthcare for medical consultants, healthcare policy makers, researchers in the field of medical biotechnology, oncology and pharmaceutical research and development.

Audience:

medical consultants, healthcare policy makers (government and private sector), researchers in the field of medical biotechnology, oncology and pharmaceutical research and development.

Preface

Machine learning in healthcare is a growing area of application of artificial intelligence in medicine. It is used in many areas covering classification and prediction problems. Artificial intelligence can be considered as systems or machines that aim to imitate the cognitive functions of people and improve themselves iteratively with the information they collect. The classification methods in the machine learning field, which is quite popular among artificial intelligence methods, can be used especially for various health data. In general, there are two types of classification approaches. The first is the binary classification approach, which sets the class tags as 0 or 1. The second is the methods that not only identify class labels but also determine class possibilities. The most prominent method for the first approach is the support vector machines method. Artificial neural networks, k-nearest neighbors, decision trees, and logistic regression methods are the methods included in the second approach. The logistic regression method is one of the most prominent methods among these methods. The logistic regression method performs the classification task by determining which class the data belongs to. The fact that this probability is close to 1 indicates that it increases the probability of being included in the related class and that it is close to 0 decreases the probability. The logistic regression method is used for early detection, diagnosis, and treatment in the field of health, from radiology to cancer, from neurology to cardiology, as well as outcome prediction and prognosis evaluation.

Mathematical modeling is used to have a better understanding and sometimes even to predict a pattern and results of the biological studies, and like all others, biological studies are no exception to this fact. Different methods of modeling are used dependent on the study and raw data available. The book will also contain several practical applications of how decision-making theory could be used in solving problems related to the selection of the best alternatives. In addition to machine learning, the book will focus on assisting decision-makers (government, organizations, companies, the general public, etc.) in making the best and most appropriate decision when confronted with multiple alternatives. The purpose of the analytical MCDM techniques is to support decision-makers under uncertainty and conflicting criteria while making a logical decision. Finally, the detail provided in the book will be of great help to the general public in their day-to-day life. The knowledge of the alternatives of the real-life problems, properties of their parameters, and the priority given to the parameters have a great effect on the consequences of the decisions. In this book, the application of MCDM has been provided for the real-life problems that occurred in health and biomedical engineering issues. In addition, the application of MCDM examples will be shown manually to users.

Ilker Ozsahin

&

Dilber Uzun Ozsahin
Department of Biomedical Engineering
Faculty of Engineering, Near East University
Nicosia/TRNC, Mersin 10
Turkey

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