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Publikācija: Risk Prediction System for Pharmacological Problems

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Nosaukums oriģinālvalodā Risk Prediction System for Pharmacological Problems
Pētniecības nozare 2. Inženierzinātnes un tehnoloģijas
Pētniecības apakšnozare 2.2. Elektrotehnika, elektronika, informācijas un komunikāciju tehnoloģijas
Autori Arnis Kiršners
Edgars Liepiņš
Sergejs Paršutins
Jānis Kūka
Arkādijs Borisovs
Atslēgas vārdi Short time series, clusterization, classification, decision trees, conditional rules, prediction
Anotācija This work considers the results of laboratory investigations carried out to create a system for predicting cardiac necrosis risks that would be based on algorithms and procedures of data mining. Con tinuous data that indicated changes in the heartbeat and descriptive characteristics of the test animals were used. The procedures of data mining used included the selection of attributes, preprocessing, clusterization, classification, forecasting, and the data analysis. The belonging of an object to a partic ular group is found out during the clusterization and preprocessing of continuous data. Correlation among different descriptive characteristics of the animals is determined. The correlation between the continuous data and descriptive characteristics is found using a classification whose results are inte grated in the form of conditional rules with the evaluation of the cardiac necrosis risks obtained in the laboratory. The resulted conditional rules and descriptive characteristics of the test animals provide the basis for predicting the cardiac necrosis risks.
DOI: 10.3103/S0146411612020046
Hipersaite: http://www.springerlink.com/content/67v61554085tl220/?MUD=MP 
Atsauce Kiršners, A., Liepiņš, E., Paršutins, S., Kūka, J., Borisovs, A. Risk Prediction System for Pharmacological Problems. Automatic Control and Computer Sciences, 2012, Vol.46, No.2, 57.-65.lpp. ISSN 0146-4116. Pieejams: doi:10.3103/S0146411612020046
Papildinformācija Citējamību skaits:
  • Scopus  0
ID 12979