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

Neshat, Mehdi; Sargolzaei, Mehdi; Toosi, Adel Nadjaran; Masoumi, Azra

doi:10.5402/2012/609718

Cited by 33 publications

(20 citation statements)

References 19 publications

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“…We now compare our results with other studies [4] as shown in Table 5. It is evident from the table that our method offers comparable results with 80 simulations at present.…”

Section: Resultssupporting

confidence: 59%

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Hepatitis Disease Diagnosis Using Multiple Imputation and Neural Network with Rough Set Feature Reduction

Mitra

Samanta

2015

Advances in Intelligent Systems and Computing

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Abstract. Intelligent automated decision support systems are found to be useful for early detection of hepatitis for augmenting survivability. We present here an intelligent system for hepatitis disease diagnosis using UCI data set for experiment. We use multiple imputation technique for managing missing values in the UCI data set. One of the potential tools in this context is neural network for classification. For better diagnostic classification accuracy, various feature selection techniques are deployed as prerequisite. These features are considered to be more informative to the doctors for taking final decision. This work attempts rough set-based feature selection (RS) technique. For classification, we use incremental back propagation learning network (IBPLN), and LevenbergMarquardt (LM) classification tested on UCI data base. We compare classification results in terms of classification accuracy, specificity, sensitivity and receiver-operating characteristics curve area(AUC).

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“…We now compare our results with other studies [4] as shown in Table 5. It is evident from the table that our method offers comparable results with 80 simulations at present.…”

Section: Resultssupporting

confidence: 59%

“…We use multiple imputation technique for missing values in the data set. This is a novel approach compared to the previous studies on hepatitis disease diagnosis [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Hepatitis Disease Diagnosis Using Multiple Imputation and Neural Network with Rough Set Feature Reduction

Mitra

Samanta

2015

Advances in Intelligent Systems and Computing

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“…Gradient descent technique has been used to optimize a model to solve the problem of feature selection, called grafting [9]. Similarity measurement in this research was using weighted Euclidean distance method.…”

Section: Introductionmentioning

confidence: 99%

Hepatitis Diagnosis Using Case-Based Reasoning with Gradient Descent as Feature Weighting Method

Bagi¹,

Suprapto

2018

JISEBI

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Retrieval is one of the stages in case-based reasoning system which find a solution to new problem or case by measuring the similarity between the new case and old cases in the case base. Some of the similarity measurement techniques are involving feature weights that show the importance of the feature in a case. Feature weights can be obtained from a domain expert or by using a feature weighting method either locally or globally. Gradient descent is the feature weighting method which computes global weights for each feature. This research implemented gradient descent to obtain feature weights in case-based reasoning for hepatitis diagnosis and the similarity measurement using weighted Euclidean distance. There were four variations of case base size and test data used in the research; i.e., 50% of case base and 50% of test data, 60% of case base and 40% of test data, 70% of case base and 30% of test data, and 80% of case base and 20% of test data were variation 1, 2, 3, and 4 respectively. In addition, each variation used four scenarios based on the way how to mark the test data. In scenario 1, 2, 3 and 4, the test data were respectively marked at the end, the beginning, the half of beginning and half of end, and the middle. The result showed that the accuracy of the system reaches 100% at scenario 1 in variation 4. Overall of all four variations and four kinds of scenario, the average accuracy of the system was 77.55%, average recall of system was 69.74%, and the average of precision was 78.39%. In addition, the level of accuracy was also influenced by the number of case base and the scenario of case selection for the case base. This is because more cases in the case base, the bigger chance for the system to find similar cases.

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“…On the other hand, it is very hard to get an appointment from a specialist; it may take some days, week or months as well. Most likely, the disease may have affected the patient most before the patient refers to the specialist for diagnosis [4]. Most of the high-risk disorders could be treated only in the initial stage.…”

Section: Introductionmentioning

confidence: 99%

Decision Support System for Hepatitis Disease Diagnosis using Bayesian Network

Lakho

Jalbani

Vighio

et al. 2017

SJCMS

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Medical judgments are tough and challenging as the decisions are often based on deficient and ambiguous information. Moreover, the result of decision process has direct effects on human lives. Act of human decision declines in emergency situations due to complication, time limit and high risks. Therefore, provision of medical diagnosis plays a dynamic role, specifically in preliminary stage when a physician has limited diagnosis experience and identifies the directions to be taken for the treatment process. Computerized Decision Support Systems have brought a revolution in the medical diagnosis. These automatic systems support the diagnosticians in the course of diagnosis. The major role of Decision Support Systems is to support the medical personnel in decision making procedures regarding disease diagnosis and treatment recommendation. The proposed system provides easy support in Hepatitis disease recognition. The system is developed using the Bayesian network model. The physician provides the input to the system in the form of symptoms stated by patient. These signs and symptoms match with the casual relationships present in the knowledge model. The Bayesian network infers conclusion from the knowledge model and calculates the probability of occurrence of Hepatitis B, C and D disorders.

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

Cited by 33 publications

References 19 publications

Hepatitis Disease Diagnosis Using Multiple Imputation and Neural Network with Rough Set Feature Reduction

Hepatitis Disease Diagnosis Using Multiple Imputation and Neural Network with Rough Set Feature Reduction

Hepatitis Diagnosis Using Case-Based Reasoning with Gradient Descent as Feature Weighting Method

Decision Support System for Hepatitis Disease Diagnosis using Bayesian Network

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