A hybrid diagnosis model for determining the types of the liver disease

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Abstract

The symptoms of liver diseases are not apparent in the initial stage, and the condition is usually quite serious when the symptoms are obvious enough. Most studies on liver disease diagnosis focus mainly on identifying the presence of liver disease in a patient. Not many diagnosis models have been developed to move beyond the detection of liver disease. The study accordingly aims to construct an intelligent liver diagnosis model which integrates artificial neural networks, analytic hierarchy process, and case-based reasoning methods to examine if patients suffer from liver disease and to determine the types of the liver disease.

Introduction

Liver is the largest internal organ in the human body, playing a major role in metabolism and serving several vital functions like decomposition of red blood cells. Liver disease usually caused by inflammation or damaged hepatocytes, registers a tenacious presence on the list of top ten fatal diseases in Taiwan. On the worldwide scale, live cancer, or more specifically hepatocellular carcinoma (HCC), remains the third most common cause of cancer-related deaths and the fifth-most frequent cancer with an estimated 560,000 new cases every year [1]. Symptoms of liver diseases tend to be negligible in the initial stage, and the condition is usually quite serious when the symptoms finally become obvious enough to attract attention. Early, effective detection of liver diseases are thus an issue of paramount importance.

Medical diagnosis relies to a great extent on a physician’s practical experiences. Yet it may take years for a physician, especially a new or junior one, to accumulate sufficient experiences. To help physicians tackle with diagnostic problems, various disease diagnosis models based on statistics and classification have been developed as they treat medical diagnosis as a decision making process during which the physician induces the diagnosis of a new and unknown case from his or her clinical experience.

Most previous researches on the development of disease diagnosis models use statistical methods for modeling. Such approaches, however, require assumptions and are usually adopted to analyze linear data. They are thus less capable of handling massive and complicated nonlinear and dependent data [2], [3]. Recent studies have accordingly adopted potentially more effectual alternatives like genetic algorithms (GA), artificial neural networks (ANN), rough sets, and support vector machines (SVM). However, most of these studies tend to concentrate on identifying the presence of a certain disease in a patient. Not many diagnosis models have so far been developed to move beyond the detection of a disease to suggest the nature, i.e. the type, of the disease.

This study accordingly introduces an intelligent liver diagnosis model (ILDM) using ANN to distinguish between healthy and diseased liver. Furthermore, analytic hierarchy process (AHP) is integrated with case-based reasoning (CBR) to diagnose the types of liver disease in patients. The constructed model can be expected to help physicians achieve more accurate and comprehensive liver diagnosis.

The structure of this paper is organized as follows. Section 2 presents a comprehensive literature review on liver disease diagnosis with an introduction to the ANN and CBR techniques. Section 3 outlines the architecture of the proposed ILDM. Section 4 details the experiments and results of liver disease diagnosis, and Section 5 summarizes the paper with brief conclusions and suggestions for future researches.

Section snippets

Artificial neural networks (ANN)

ANN, originally derived from neurobiological models, is a system composed of highly interconnected and interacting processing units. The most important feature of neural networks is their ability to learn. Just like human brain, neural networks can learn by example and dynamically modify themselves to fit the data presented. Therefore, ANN has attracted many researchers and emerged as one of the most popular tools for pattern recognition and classification [4]. Classification rules are useful

The proposed ILDM (intelligent liver diagnosis model)

Disease diagnosis mainly relies on the physician’s clinical experience and relevant examinations. In order to simplify the diagnosis and enable the physician to make a judgment about the existence of liver disease, the study develops an ILDM that incorporates two processing phases: an initial, classifying phase for examining the existence of a liver disease and a subsequent, concluding phase for diagnosing the type of the existing liver disease. In the classifying phase, ANN is adopted to

Experiment and result

Essential health examination data of 510 outpatient visitors to a medical center in Taiwan during the 1 year from March 2005 to February 2006 was collected as the cases in the dataset. With the assistance of doctors and medical specialists of liver conditions, 210 cases were found to be healthy, and 300 cases were diagnosed with liver conditions (chronic hepatitis, alcohol hepatitis, liver cirrhosis, B hepatitis, and others). The health examination data incorporated the results of an examinee’s

Conclusion

In spite of the constant advancement in the field of medical sciences, diagnosis of diseases remains a challenging task. Liver disease in particular is not easily discovered at its initial stage; early diagnosis of this leading cause of mortality is therefore highly important. As a part of the ongoing efforts to make diagnosis more effective and efficient, this study accordingly develops a two-phase intelligent diagnosis model aiming to provide a comprehensive analytic framework to raise the

Conflict of interest statement

None declared.

Acknowledgements

We would like to thank Chun-Ming Huang for collecting questionnaire data and providing clinical attributes’ weights. We are grateful to the five physicians who participated in the survey. We also appreciate the two anonymous reviewers for their valuable comments.

Chun-Ling Chuang is an assistant professor in the Department of Information Management at Kainan University, Taiwan. She received her M.S. and Ph.D. degrees in Department of Industrial Engineering from the University of Iowa. Her present research interests include applications of hybrid soft computing models in data mining, optimal solution, and economical models for decision making, knowledge management and classification.

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    Chun-Ling Chuang is an assistant professor in the Department of Information Management at Kainan University, Taiwan. She received her M.S. and Ph.D. degrees in Department of Industrial Engineering from the University of Iowa. Her present research interests include applications of hybrid soft computing models in data mining, optimal solution, and economical models for decision making, knowledge management and classification.

    Rong-Ho Lin is an associate professor in the Department of Industrial Engineering and Management at National Taipei University of Technology. He received the Bachelor degree in Chun-Yuan Christian University, Taiwan, the M.S. degree in Department of Mathematics and Computer Science from Minnesota State University, and the Ph.D. degree in the Industrial Management and Engineering from the University of Iowa. He has worked in various enterprises for 10 years and his research activities include applications of artificial intelligence and hybrid soft computing models in decision making and supply chain management.

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