Classification and Prediction with Neural Networks

Classification and Prediction with Neural Networks

Arnošt Veselý (Czech University of Life Sciences, Czech Republic)
DOI: 10.4018/978-1-60566-218-3.ch004
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This chapter deals with applications of artificial neural networks in classification and regression problems. Based on theoretical analysis it demonstrates that in classification problems one should use cross-entropy error function rather than the usual sum-of-square error function. Using gradient descent method for finding the minimum of the cross entropy error function, leads to the well-known backpropagation of error scheme of gradient calculation if at the output layer of the neural network the neurons with logistic or softmax output functions are used. The author believes that understanding the underlying theory presented in this chapter will help researchers in medical informatics to choose more suitable network architectures for medical applications and that it helps them to carry out the network training more effectively.
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Medicine involves decision-making and classification or prediction is an important part of it. However, medical classification or prediction is usually a very complex and hard process at least from the following reasons:

  • Much of the data that are relevant to classification or prediction, especially those received from laboratories, are complex or difficult to comprehend and can be interpreted only by experts.

  • For reliable classification or prediction a large amount of data is frequently needed and some important anomalies in the data may be overlooked.

  • When monitoring a patient, some for the patient dangerous events can be too rare and therefore it may be difficult to identify them in the continuous stream of data.

Thus computer-assisted support could be of significant help. With the increasing number of clinical databases it is likely that machine-learning applications will be necessary to detect rare conditions and unexpected outcomes. The needful methods and algorithms can be found first of all in the domain of mathematical statistics and artificial intelligence. From means of artificial intelligence rule based experts systems were primarily used. Today the most important applications utilize algorithms based on neural networks, fuzzy systems, neurofuzzy systems or evolution algorithms.

Classical statistical methods require certain assumptions about the distribution of data. Neural networks can constitute a good alternative when some of these assumptions cannot be verified. From this point of view neural networks constitute a special kind of nonparametric statistical methods. Therefore the most successful neural network architectures are implemented in standard statistical software packages, as there is for example STATISTICA. Thus the eligibility of neural network algorithms for the given decision making task can be easily tested on the sample data using one of these software packages.

From abstract mathematical point of view medical classification and prediction tasks fall into the scope of either classification or regression problems. A classification or pattern recognition can be viewed as a mapping from a set of input variables to an output variable representing the class label. In classification problems the task is to assign new inputs to labels of classes or categories. In a regression problem we suppose that there exists underlying continuous mapping y = f(x) and we estimate the unknown value of y using the known value of x.

Typical case of classification problem in medicine is medical diagnostics. As input data the patient’s anamnesis, subjective symptoms, observed symptoms and syndromes, measured values (e.g. blood pressure, body temperature etc.) and results of laboratory tests are taken. This data are coded by vector x, the components of which are binary or real numbers. The patients are classified into categories D1, . . ., Dm that correspond to their possible diagnoses d1, . . ., dm .

Many successful applications of neural networks in medical diagnostics can be found in literature (Gant, 2001). The processing and interpretation of electrocardiograms (ECG) with neural networks was intensively studied, because evaluation of long term ECG recordings is a time consuming procedure and requires automated recognition of events that occur infrequently (Silipo, 1998). In radiology neural networks have been successfully applied to X-ray analysis in the domains of chest radiography (Chen, 2002) and (Coppini, 2003), mammography (Halkiotis, 2007) and computerized tomography (Lindahl, 1997), (Gletsos, 2003) and (Suzuki, 2005). Also classification of ultrasound images was successful (Yan Sun, 2005). Neural networks have been also successfully applied to diagnose epilepsy (Walczak, 2001) or to detect seizures from EEG patterns (Alkan, 2005).

Also prediction of the patient’s state can be stated as classification problem. On the basis of examined data represented with vector x patients are categorized into several categories P1, . . ., Pm that correspond to different future states. For example five categories with the following meaning can be considered: P1 can mean death, P2 deterioration of the patient’s state, P3 steady state, P4 improvement of the patient’s state and P5 recovery.

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Editorial Advisory Board
Table of Contents
Riccardo Bellazzi
Petr Berka, Jan Rauch, Djamel Abdelkader Zighed
Petr Berka, Jan Rauch, Djamel Abdelkader Zighed
Chapter 1
Jana Zvárová, Arnošt Veselý
This chapter introduces the basic concepts of medical informatics: data, information, and knowledge. Data are classified into various types and... Sample PDF
Data, Information and Knowledge
Chapter 2
Michel Simonet, Radja Messai, Gayo Diallo
Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status... Sample PDF
Ontologies in the Health Field
Chapter 3
Alberto Freitas, Pavel Brazdil, Altamiro Costa-Pereira
This chapter introduces cost-sensitive learning and its importance in medicine. Health managers and clinicians often need models that try to... Sample PDF
Cost-Sensitive Learning in Medicine
Chapter 4
Arnošt Veselý
This chapter deals with applications of artificial neural networks in classification and regression problems. Based on theoretical analysis it... Sample PDF
Classification and Prediction with Neural Networks
Chapter 5
Patrik Eklund, Lena Kallin Westin
Classification networks, consisting of preprocessing layers combined with well-known classification networks, are well suited for medical data... Sample PDF
Preprocessing Perceptrons and Multivariate Decision Limits
Chapter 6
Xiu Ying Wang, Dagan Feng
The rapid advance and innovation in medical imaging techniques offer significant improvement in healthcare services, as well as provide new... Sample PDF
Image Registration for Biomedical Information Integration
Chapter 7
ECG Processing  (pages 137-160)
Lenka Lhotská, Václav Chudácek, Michal Huptych
This chapter describes methods for preprocessing, analysis, feature extraction, visualization, and classification of electrocardiogram (ECG)... Sample PDF
ECG Processing
Chapter 8
EEG Data Mining Using PCA  (pages 161-180)
Lenka Lhotská, Vladimír Krajca, Jitka Mohylová, Svojmil Petránek, Václav Gerla
This chapter deals with the application of principal components analysis (PCA) to the field of data mining in electroencephalogram (EEG) processing.... Sample PDF
EEG Data Mining Using PCA
Chapter 9
Darryl N. Davis, Thuy T.T. Nguyen
Risk prediction models are of great interest to clinicians. They offer an explicit and repeatable means to aide the selection, from a general... Sample PDF
Generating and Verifying Risk Prediction Models using Data Mining
Chapter 10
Vangelis Karkaletsis, Konstantinos Stamatakis, Karampiperis, Karampiperis, Pythagoras Karampiperis, Pythagoras Karampiperis
The World Wide Web is an important channel of information exchange in many domains, including the medical one. The ever increasing amount of freely... Sample PDF
Management of Medical Website Quality Labels via Web Mining
Chapter 11
Rainer Schmidt
In medicine, a lot of exceptions usually occur. In medical practice and in knowledge-based systems, it is necessary to consider them and to deal... Sample PDF
Two Case-Based Systems for Explaining Exceptions in Medicine
Chapter 12
Bruno Crémilleux, Arnaud Soulet, Jiri Kléma, Céline Hébert, Olivier Gandrillon
The discovery of biologically interpretable knowledge from gene expression data is a crucial issue. Current gene data analysis is often based on... Sample PDF
Discovering Knowledge from Local Patterns in SAGE Data
Chapter 13
Jirí Kléma, Filip Železný, Igor Trajkovski, Filip Karel, Bruno Crémilleux
This chapter points out the role of genomic background knowledge in gene expression data mining. The authors demonstrate its application in several... Sample PDF
Gene Expression Mining Guided by Background Knowledge
Chapter 14
Pamela L. Thompson, Xin Zhang, Wenxin Jiang, Zbigniew W. Ras, Pawel Jastreboff
This chapter describes the process used to mine a database containing data, related to patient visits during Tinnitus Retraining Therapy. The... Sample PDF
Mining Tinnitus Database for Knowledge
Chapter 15
Dinora A. Morales, Endika Bengoetxea, Pedro Larrañaga
Infertility is currently considered an important social problem that has been subject to special interest by medical doctors and biologists. Due to... Sample PDF
Gaussian-Stacking Multiclassifiers for Human Embryo Selection
Chapter 16
Mining Tuberculosis Data  (pages 332-349)
Marisa A. Sánchez, Sonia Uremovich, Pablo Acrogliano
This chapter reviews the current policies of tuberculosis control programs for the diagnosis of tuberculosis. The international standard for... Sample PDF
Mining Tuberculosis Data
Chapter 17
Mila Kwiatkowska, M. Stella Atkins, Les Matthews, Najib T. Ayas, C. Frank Ryan
This chapter describes how to integrate medical knowledge with purely inductive (data-driven) methods for the creation of clinical prediction rules.... Sample PDF
Knowledge-Based Induction of Clinical Prediction Rules
Chapter 18
Petr Berka, Jan Rauch, Marie Tomecková
The aim of this chapter is to describe goals, current results, and further plans of long-time activity concerning application of data mining and... Sample PDF
Data Mining in Atherosclerosis Risk Factor Data
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