• Journal of information and communication convergence engineering
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• v.15 no.2
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• pp.85-90
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• 2017

Expert systems for health diagnosis are only for medical experts who have deep knowledge in the field but we need a self-checking pre-diagnosis system for preventive public health monitoring. Korea Traditional Medicine is popular in use among Korean public but there exist few available health information systems on the internet. A computerized self-checking diagnosis system is proposed to reduce the social cost by monitoring health status with simple symptom checking procedures especially for Korea Traditional Medicine users. Based on the national reports for disease/symptoms of Korea Traditional Medicine, we build a reliable database and devise an intelligent inference engine using fuzzy c-means clustering. The implemented system gives five most probable diseases a user might have with respect to symptoms given by the user. Inference results are verified by Korea Traditional Medicine doctors as sufficiently accurate and easy to use.

• Journal of the Korea Society of Computer and Information
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• v.19 no.5
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• pp.11-18
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• 2014

In this paper, we propose a self-diagnosis system that is based on the ART2 algorithm in order to extract more detailed disease information by fuzzy reasoning method especially when the boundary of perceived symptoms are not clearly classified into disease categories. With that modification from previous version of the self health pre-diagnosis system, the proposed one is verified as more effective by field experts' evaluation as an intelligent assistant tool for public users before they consult with medical experts.

• Journal of Intelligence and Information Systems
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• v.13 no.1
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• pp.81-90
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• 2007

This paper shows an intelligent disease diagnosis system for public. Our system deals with 30 diseases and their typical symptoms selected based on the report from Ministry of Health and Welfare, Korea. Technically, the system uses a modified FCM algorithm for clustering diseases and the input vector consists of the result of user-selected questionnaires. The modified FCM algorithm improves the quality of clusters by applying symmetrically measure based on the fuzzy theory so that the clusters are relatively sensitive to the shape of the pattern distribution. Furthermore, we extract the highest 5 diseases only related to the user-selected questionnaires based on the fuzzy membership function between questionnaires and diseases in order to avoid diagnosing unrelated disease.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.378-382
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• 2001

The purpose of this study was to develop an expert system supporting the diagnosis of diffuse interstitial lung disease by high resolution computed tomography. CLIPS(C language integrated production system) with rule-based reasoning was used to develop the system. Development of expert system had three stages knowledge acquisition, knowledge representation, and reasoning. Knowledge was obtained and integrated, from tables and figure legends of a representative textbook in the domain of this expert system, High-Resolution CT of the Lung, by Webb WR, Mueller NL, and Naidich DP. The acquired knowledge was analyzed to form a knowledge base. Overlapping knowledge was eliminated, similar pieces of knowledge were combined and professional terms were defined. The most important knowledge of findings was then selected for each disease. After groupings of combined findings were made, disease groups were analyzed sequentially to determine final diagnoses. The system was based upon the input of 69 diseases, 185 findings, 73 conditions, 387 status, and 62 rules. The system was set up to determine the diagnoses of diseases from the combination of findings using forward reasoning. In an empirical trial, the system was applied to support the diagnosis of 40 cases of diffuse interstitial lung diseases. The performance of two doctors with support of the system was compared to that of another two doctors without support of the system. The two doctors with the support of the system made more accurate diagnoses than the doctors without the support of the system. The system is believed to be useful for the diagnosis of rare diseases and for cases with many possible differential diagnoses. In conclusion, an expert system supporting the high resolution computed tomographic diagnosis of diffuse interstitial lung disease was developed and the system is thought to be useful for medical practice.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.508-510
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• 2001

Adopting the method of user weighting fuzzy mathematics, the author accomplished the subject title “Study on Expert System of Chicken\`s Common Diseases Diagnostics”, which could properly diagnose 30 kinds of chicken\`s common diseases and the accordance rate reached 80% verified through 244 disease cases. On the basis of the accomplishment, the multimedia technology was adopted further more to establish a system, which integrated with the input, display, query, and processing of sound, picture and text etc., combined with the previous chicken disease diagnostic expert system, make the output information of computer more rich and comprehensive, and the accordance rate of disease diagnosis could be improved. The system consists of database, knowledge base, graphics and picture base. This system is easy to operate and interface of which is vivid and intuitive. It could output diagnostic result and prescribe rapidly, so that, such a system is not only adapted to large, medium chicken farm but also to grass-roots veterinary station for developing health care and disease diagnosing. It is sure that the system could have side prospect of application.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.23-30
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• 2010

With the advancement of information technology and increasing diversity in medical field, there are ongoing researches on ontology based intelligent medical system in Oriental medicine field. Intelligent diagnostic support system uses ontology to give a structure to complex medical knowledge and personal medical history so that we can make diagnosis more scientific, and provide better medical services. In this paper, we suggest an ontology that structuralize three knowledge types basic medical data, clinical trial data, and personal health information, which can be used as important information for individually tailored diagnosis. Especially in Oriental medicine diagnosis, both patient's symptoms of illness and physical constitution play a great role; it can lead to distinct diagnosis depending on their combination. Thus, it is much needed to have a diagnostic support system that uses personal health history and physical constitution along with basic medical data and clinical trial data in the field. In this paper, we implemented an Oriental medicine diagnostic support system that provides individualized diagnosis service to each patient by building an ontology on Oriental medicine focused on individual physical constitution and disease information.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.243-249
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• 2012

These days it is becoming more and more common to find electronic medical screening systems installed in Oriental hospitals and clinics. This is a relatively new development for the practice of traditional Oriental medicine. Specifically, Pulse detection machines are being utilized in order to help determine a patient's disease scientifically. However, identifying and diagnosing the specific disease correctly for each patient is still very difficult in Oriental medicine. The intention of this paper is to propose a solution which uses two separate Electronic systems working together to produce a better likelihood of finding the correct diagnosis for each patient. It is proposed that an EMR intelligent electronic chart system be developed and employed, which would utilize both Pulse wave system and a tongue detection system at the same time, in order to solve the problem. Computer simulation results have proven to show that EMR systems used in hospitals and clinics are more efficient and yield a more accurate diagnosis than traditional methods.

• Journal of Internet of Things and Convergence
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• v.2 no.4
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• pp.15-20
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• 2016

This paper describes the construction of knowledge data retrieval management system based on medical image CT. The developed system is aimed to improve the efficiency of the hospital by reading the medical images using the intelligent retrieval technology and diagnosing the patient 's disease name. In this study, the medical image DICOM file of PACS is read, the image is processed, and feature values are extracted and stored in the database. We have implemented a system that retrieves similarity by comparing new CT images required for medical treatment with the feature values of other CTs stored in the database. After converting 100 CT dicom provided for academic research into JPEG files, Code Book Library was constructed using SIFT, CS-LBP and K-Mean Clustering algorithms. Through the database optimization, the similarity of the new CT image to the existing data is searched and the result is confirmed, so that it can be utilized for the diagnosis and diagnosis of the patient.

• Journal of Digital Contents Society
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• v.7 no.4
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• pp.245-249
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• 2006

In the rapidly changing high-tech society, lots of people are exposed to various kinds of stress and disease with an effort to adopt to the society, in spite of the benefits and abundance created by various technologies. Therefore, the health of modem people is our main concern and essential subject. The researcher would like to suggest systematical and intelligent medical diagnosis expert system that can give the effect same as the help from real experts with health check helper and scientific and objective knowledge that fit to the age and environment of changing.