A new hybrid case-based architecture for medical diagnosis

Hsu, Chien-Chang; Ho, Cheng-Seen

doi:10.1016/j.ins.2003.11.009

Cited by 58 publications

(27 citation statements)

References 16 publications

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“…Guiu et al [8] introduced a case-based classifier system to solve the automatic diagnosis of Mammary Biopsy Images. Hsu and Ho [9] combined the CBR, NN, fuzzy theory, and induction theory together to facilitate multiple-disease diagnosis and the learning of new adaptation knowledge. Wyns et al [10] applied a modified Kohonen mapping combined with a CBR evaluation criterion to predict early arthritis, including rheumatoid arthritis and spondyloarthropathy.…”

Section: Introductionmentioning

confidence: 99%

Hepatitis Disease Diagnosis Using Hybrid Case Based Reasoning and Particle Swarm Optimization

Neshat

Sargolzaei

Toosi

et al. 2012

ISRN Artificial Intelligence

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Correct diagnosis of a disease is one of the most important problems in medicine. Hepatitis disease is one of the most dangerous diseases that affect millions of people every year and take man’s life. In this paper, the combination of two methods of PSO and CBR (case-based reasoning) has been used to diagnose hepatitis disease. First, a case-based reasoning method is workable to preprocess the data set therefore a weight vector for every one feature is extracted. A particle swarm optimization model is then practical to assemble a decision-making system based on the selected features and diseases recognized. Many researchers have tried to have a more accurate diagnosis of the disease through the use of various methods. The data used has been taken from the site UCI called hepatitis disease. This database has 155 records and 19 fields. This method was compared with five other classification methods and given the results of the proposed method (CBR-PSO), better results were achieved. The proposed method could diagnose hepatitis disease with the accuracy of 93.25%.

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Section: Introductionmentioning

confidence: 99%

Hepatitis Disease Diagnosis Using Hybrid Case Based Reasoning and Particle Swarm Optimization

Neshat

Sargolzaei

Toosi

et al. 2012

ISRN Artificial Intelligence

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“…Table 1 outlines the diagnostic process. TABLE I. DIAGNOSTIC CYCLE [27] This diagnosis process may become easier and more reliable if equipped with an expert system that provides past diagnosis of cases, thereby helping the physician to arrive at a solution based on the past experiences [28].…”

Section: Medical Reasoningmentioning

confidence: 99%

A Survey on Case-based Reasoning in Medicine

Choudhury¹,

Begum²

2016

ijacsa

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Abstract-Case-based reasoning (CBR) based on the memorycentered cognitive model is a strategy that focuses on how people learn a new skill or how they generate hypothesis on new situations based on their past experiences. Among various Artificial Intelligence tracks, CBR, due to its intrinsic similarity with the human reasoning process has been very promising in the utilization of intelligent systems in various domains, in particular in the domain of medicine. In this paper, we extensively survey the literature on CBR systems that are used in the medical domain over the past few decades. We also discuss the difficulties of implementing CBR in medicine and outline opportunities for future work.

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“…Garrell I Guiu et al (1999) introduced a case-based classifier system to solve the automatic diagnosis of Mammary Biopsy Images. Hsu and Ho (2004) combined the CBR, NN, fuzzy theory, and induction theory together to facilitate multiple-disease diagnosis and the learning of new adaptation knowledge. Wyns et al (2004) applied a modified Kohonen mapping combined with a CBR evaluation criterion to predict early arthritis, including rheumatoid arthritis (RA) and spondyloarthropathy (SpA).…”

Section: New Tools In Soft Computingmentioning

confidence: 99%

Forecasting of manufacturing cost in mobile phone products by case-based reasoning and artificial neural network models

2010

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The mobile phone manufacturers in Taiwan have made great efforts in proposing the rational quotations to the international phone companies with the ambition to win the bids by out beating other phone manufacturers. However, there are a lot of uncertainties and issues to be resolved in estimating the manufacturing costs for mobile phone manufacturers. As far as we know, there is no existing model which can be applied directly in forecasting the manufacturing costs. This research makes the first attempt to develop a hybrid system by integrated Case-Based Reasoning (CBR) and Artificial Neural Networks (ANN) as a Product Unit Cost (PUC) forecasting model for Mobile Phone Company. According to the cost formula of the mobile phone and experts' opinions, a set of qualitative and quantitative factors are analyzed and determined. Qualitative factors are applied in CBR to retrieve a similar case from the case bases for a new phone product and ANN is used to find the relationship between the quantitative factors and the predicted PUC. Finally, intensive experiments are conducted to test the effectiveness of six different forecasting models. The model proposed in this research is compared with the other five models and the MAPE value of the proposed model is the smallest. This research provides a new prediction model with high accuracy for mobile phone manufacturing companies.

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A new hybrid case-based architecture for medical diagnosis

Cited by 58 publications

References 16 publications

Hepatitis Disease Diagnosis Using Hybrid Case Based Reasoning and Particle Swarm Optimization

Hepatitis Disease Diagnosis Using Hybrid Case Based Reasoning and Particle Swarm Optimization

A Survey on Case-based Reasoning in Medicine

Forecasting of manufacturing cost in mobile phone products by case-based reasoning and artificial neural network models

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