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portada Ensemble data mining method for predicting outbreak disease
Type
Physical Book
Author
Publisher
Language
English
Pages
128
Format
Paperback
Dimensions
22.9x15.2x0.8 cm
Weight
0.20 kg.
ISBN13
9786138921462
Categories

Ensemble data mining method for predicting outbreak disease

Hakizimana Leopord (Author) · Scholars' Press · Paperback

Ensemble data mining method for predicting outbreak disease - Leopord, Hakizimana

New Book Imported to Netherlands
Delivery: 25 Sep - 29 Sep Shipping: 13 to 14 business days.
€ 64,84
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Synopsis "Ensemble data mining method for predicting outbreak disease"

Data mining has played a significant role in outbreak disease prediction. However, issues persist due to gaps in existing research based on concerns about low predictive accuracy. Some algorithms of classification method are weak and the classification methods applied to malaria outbreak prediction are still very rare and more are needed to be established. To fill these gaps, the overall goal of this book is to propose an ensemble data mining classification method for malaria outbreak prediction that an organization can adopt to obtain better results. The research used qualitative and quantitative approaches to examine existing results and literature from various sources. Weka software as a machine learning tool has been used to ensemble DT (LF), LWL, SMO and complement Naive Bayes algorithms. The results of the study showed that the ensemble data mining classification method for the prediction of malaria outbreak is very accurate and powerful as compared to traditional algorithms.

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