Review on Data Mining Techniques in Healthcare Sector

Kaur, Shubpreet; Bawa, Rajesh K.

doi:10.1109/i-smac.2018.8653795

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…Furthermore, this model is easy to build because it uses the probabilistic conditional approach to analyse datasets by multiplying the individual probabilities of each pair of value attributes, which will help the user derive more functionality from the data without being over-fitted even for large datasets [30], [25]. This classifier is very stable; it can be trained with small datasets to create accurate parameter predictions since it only needs the calculation from the frequencies and the outcomes of the pairs of attributes in the training datasets [32], [27].…”

Section: ) Naïve Bayesmentioning

confidence: 99%

“…Based on 10 papers, Decision Tree is the highest algorithm used for pandemic outbreak datasets. Besides, [27] stated that the automatic selection of features and the reduction of complexity can be carried out by Decision Tree classifiers, where the tree structure provides easy-to-understand and comprehensible information on the predictive or generation capability of classification. [31] mentioned that results from many researchers show that Naïve Bayes has excellent performance in terms of computational efficiency since it has better performance than other algorithms as it can handle missing data and produce high accuracy in prediction.…”

Section: B Data Mining Techniques In Pandemic Outbreaksmentioning

confidence: 99%

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Data Mining Techniques for Pandemic Outbreak in Healthcare

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Mohamed

Ali

2021

JOIV : Int. J. Inform. Visualization

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Pandemic outbreaks such as SARS-CoV, MERS-CoV and Covid-19 have attracted worldwide attention since these viruses have affected many countries and become a global public health issue. In 2019, Covid-19 was announced as a pandemic disease and categorized as a public health emergency globally. It is ranked as the sixth most serious pandemic internationally. This pandemic tracking and analysis require an appropriate method that gives better performance in terms of accuracy, precision and recall that defines its pattern since it involves huge and complicated datasets from the pandemic. Pattern identification is currently applied in many instances due to the rapid growth of data besides having the potential to generate a knowledge-rich environment which can help to significantly improve the quality of clinical decisions and identify the relationships between data items. Therefore, there is a need to review the techniques in data mining on the pandemic outbreak that focuses on healthcare. The goal of this study was to analyze the algorithms from the data mining method that had been implemented for pandemic outbreaks in past research such as SARS-CoV, MERS-CoV and Covid-19. The result shows that 2 main algorithms, namely Naïve Bayes and Decision Tree, from the classification method, are appropriate algorithms and give more than 90% accuracy in both the pandemic and healthcare. This will be further considered and investigated for future analysis on large datasets of Covid-19 which can help researchers and healthcare practitioners in controlling the infection of the coronavirus using the data mining technique discussed.

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