Application of Birth Defect Prediction Model Based on C5.0 Decision Tree Algorithm

Guo, Junmei; Liu, Haiying; Luan, Yizhong

doi:10.1109/cybermatics_2018.2018.00310

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…For example, a neural network algorithm can handle a large and complex dataset but the time complexity would be high and for most cases that is not the ideal solution. [16] has mention the weakness of time series analysis and naïve Bayes classifier has the limitation of needing a large number of data to obtain a good result [23].…”

Section: Single Algorithmsmentioning

confidence: 99%

“…As the work mentioned before, the genetic algorithm plays an important role in these cases where it optimizes the parameters for the neural network algorithm [29]. In a previous work done, they are able to solve the problem of low speed in local optimization and convergence of backpropagation neural network [23] and another one was able to achieve high accuracy for their prediction with the deployment of genetic algorithm [31].…”

Section: Hybrid Algorithmsmentioning

confidence: 99%

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Systematic review on event prediction models

Jamaludin

Ramasamy

2020

IJEECS

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Predicting an event can comes as a huge advantage for a lot of people, especially if it involves profits that a company can make. In this paper, matters that were discussed were the algorithms used for the event prediction systems, the type of algorithms and the comparison between different types of algorithm. Two main categories that were compared was the single algorithm for the prediction model and hybrid algorithms which is a combination of two or more algorithms in the model. Hybrid algorithm shows signs of better efficiency when data used were complex and large but a smaller dataset is more suitable with a single algorithm as the base as it is easier to apply with satisfactory results.

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Section: Single Algorithmsmentioning

confidence: 99%

Section: Hybrid Algorithmsmentioning

confidence: 99%