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A privacy-preserving group decision making expert system for medical diagnosis based on dynamic knowledge base.

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Title: A privacy-preserving group decision making expert system for medical diagnosis based on dynamic knowledge base.
Authors: Li, Wuyungerile1 (AUTHOR) Gerile@imu.edu.cn, Zong, Na1 (AUTHOR), Liu, Kaifeng1 (AUTHOR), Li, Pengyu1 (AUTHOR), Ma, Xuebin1 (AUTHOR)
Source: Wireless Networks (10220038). Oct2024, Vol. 30 Issue 7, p6237-6247. 11p.
Subjects: Euclidean algorithm, Group decision making, Knowledge base, Data conversion, Euclidean distance
Abstract: Most of the present researches on wise medical incorporate a static supporting data, while medical knowledge and rules are featured with a dynamic change, which affect calculation efficiency and accuracy of the result. This paper combined data mining technology with expert system framework, established a medical expert system with a dynamic knowledge base. In the innovative dynamic system, samples were together with their classification results would be added to the knowledge base. In addition, privacy protection was considered in the expert system by means of data removal and conversion of attribute value. At last, for a comprehensive and precise evaluation of both numeric and character attribute data, we proposed a new similarity measurement algorithm termed as E–JD, which combinate Euclidean Distance algorithm with Jaccard Distance algorithm. For more improvement of the data classifasion accuracy, based on KNN and the dynamic knowledge base, the innovative algorithm of D-SNN was proposed, with a modification in the selection and decision of the nearest neighbor objects. The experimental comparison between 1NN, KNN, KNN-means and D-SNN algorithm, shows that the classification performance of the proposed algorithm, D-SNN, exhibits an impressive improvement. [ABSTRACT FROM AUTHOR]
Copyright of Wireless Networks (10220038) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Wireless+Networks+%2810220038%29%22">Wireless Networks (10220038)</searchLink>. Oct2024, Vol. 30 Issue 7, p6237-6247. 11p.
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  Data: Most of the present researches on wise medical incorporate a static supporting data, while medical knowledge and rules are featured with a dynamic change, which affect calculation efficiency and accuracy of the result. This paper combined data mining technology with expert system framework, established a medical expert system with a dynamic knowledge base. In the innovative dynamic system, samples were together with their classification results would be added to the knowledge base. In addition, privacy protection was considered in the expert system by means of data removal and conversion of attribute value. At last, for a comprehensive and precise evaluation of both numeric and character attribute data, we proposed a new similarity measurement algorithm termed as E–JD, which combinate Euclidean Distance algorithm with Jaccard Distance algorithm. For more improvement of the data classifasion accuracy, based on KNN and the dynamic knowledge base, the innovative algorithm of D-SNN was proposed, with a modification in the selection and decision of the nearest neighbor objects. The experimental comparison between 1NN, KNN, KNN-means and D-SNN algorithm, shows that the classification performance of the proposed algorithm, D-SNN, exhibits an impressive improvement. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Wireless Networks (10220038) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11276-020-02374-4
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        Text: English
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      – SubjectFull: Knowledge base
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      – SubjectFull: Data conversion
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      – SubjectFull: Euclidean distance
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              Text: Oct2024
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