Clinical Decision Support Systems: From the Perspective of Small and Imbalanced Data Set.
Clinical decision support systems are data analysis software that supports health professionals' decision - making the process to reach their ultimate outcome, taking into account patient information. However, the need for decision support systems cannot be denied because of most activities in the f...
| Publicado en: | Studies in Health Technology & Informatics Vol. 262; pp. 344 - 348 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137369860&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137369860 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2019 vid: 262 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 137369860 137369860 137369860 10.3233/SHTI190089 137369860 ppf: 344 ppct: 4 formats: tig: atl: Clinical Decision Support Systems: From the Perspective of Small and Imbalanced Data Set. aug: au: PAR, Oznur Esra SEZER, Ebru AKCAPINAR SEVER, Hayri affil: Turkish Aerospace sug: subj: Decision Support Systems, Clinical Data Analytics Data Management Methods Statistics Human Machine Learning Minimum Data Set Algorithms Classification ab: Clinical decision support systems are data analysis software that supports health professionals' decision - making the process to reach their ultimate outcome, taking into account patient information. However, the need for decision support systems cannot be denied because of most activities in the field of health care within the decision-making process. Decision support systems used for diagnosis are designed based on disease due to the complexity of diseases, symptoms, and diseasesymptoms relationships. In the design and implementation of clinical decision support systems, mathematical modeling, pattern recognition and statistical analysis techniques of large databases and data mining techniques such as classification are also widely used. Classification of data is difficult in case of the small and /or imbalanced data set and this problem directly affects the classification performance. Small and/or imbalance dataset has become a major problem in data mining because classification algorithms are developed based on the assumption that the data sets are balanced and large enough. Most of the algorithms ignore or misclassify examples of the minority class, focus on the majority class. Most health data are small and imbalanced by nature. Learning from imbalanced and small data sets is an important and unsettled problem. Within the scope of the study, the publicly accessible data set, hepatitis was oversampled by distance-based data generation methods. The oversampled data sets were classified by using four different machine learning algorithms. Considering the classification scores of four different machine learning algorithms (Artificial Neural Networks, Support Vector Machines, Naive Bayes and Decision Tree), optimal synthetic data generation rate is recommended. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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