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. 2020 Dec 12:2020:6680002.
doi: 10.1155/2020/6680002. eCollection 2020.

Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome

Rashid Naseem  1 , Bilal Khan  2 , Muhammad Arif Shah  1 , Karzan Wakil  3 , Atif Khan  4 , Wael Alosaimi  5 , M Irfan Uddin  6 , Badar Alouffi  7
Affiliations

Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome

Rashid Naseem et al. J Healthc Eng. .

Abstract

In the recent era, a liver syndrome that causes any damage in life capacity is exceptionally normal everywhere throughout the world. It has been found that liver disease is exposed more in young people as a comparison with other aged people. At the point when liver capacity ends up, life endures just up to 1 or 2 days scarcely, and it is very hard to predict such illness in the early stage. Researchers are trying to project a model for early prediction of liver disease utilizing various machine learning approaches. However, this study compares ten classifiers including A1DE, NB, MLP, SVM, KNN, CHIRP, CDT, Forest-PA, J48, and RF to find the optimal solution for early and accurate prediction of liver disease. The datasets utilized in this study are taken from the UCI ML repository and the GitHub repository. The outcomes are assessed via RMSE, RRSE, recall, specificity, precision, G-measure, F-measure, MCC, and accuracy. The exploratory outcomes show a better consequence of RF utilizing the UCI dataset. Assessing RF using RMSE and RRSE, the outcomes are 0.4328 and 87.6766, while the accuracy of RF is 72.1739% that is also better than other employed classifiers. However, utilizing the GitHub dataset, SVM beats other employed techniques in terms of increasing accuracy up to 71.3551%. Moreover, the comprehensive outcomes of this exploration can be utilized as a reference point for further research studies that slight assertion concerning the enhancement in extrapolation through any new technique, model, or framework can be benchmarked and confirmed.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Methodology workflow diagram.
Figure 2
Figure 2
Number of liver patient and nonliver patient for each dataset: (a) UCI ML repository; (b) GitHub repository.
Figure 3
Figure 3
Specificity, precision, recall, F-measure, G-measure, and MCC analysis representation.
Figure 4
Figure 4
Accuracy achieved via each classifier.
Figure 5
Figure 5
Accuracy percentage difference between RF and other employed classifiers.
Figure 6
Figure 6
Specificity, recall, precision, MCC, F-measure, and G-measure analysis representation.
Figure 7
Figure 7
Accuracy representation.
Figure 8
Figure 8
Accuracy percentage difference between SVM and other employed classifiers.
See this image and copyright information in PMC

References

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