Algorithm C4.5, Cat, Healthy


One of the activities in the TIU (Technical Implementing Unit) of Office of Animal Husbandry and Animal Health is to carry out the examination of pet health, record and issue an Animal Health Certificate (AHC). The object in this research was cat, where the examination was still performed by laboratory test, using paper as a form of diagnosis carried out by a vet and not using the technology. Therefore, an application to provide decision in determining healthy cat that also implements the algorithm C4.5 based on android system was designed. This system was able to perform diagnosis of cat diseases quickly based on previous medical record, with a training history (training data) as many as 44 cases. Then the classification process would strengthen the results of cat disease diagnosis such as how to deal with disease that had similar symptoms so as to facilitate the Office of Animal Husbandry and Animal Health, as a comparison to issue an Animal Health Certificate. Based on the Black Box testing, the functionality of the module was in accordance with the needs of the system, while the accuracy testing generated the percentage value of 93.18%. This shows that the algorithm C4.5 has a good accuracy to determine healthy cat.


S. J. Lee, Z. Xu, T. Li, and Y. Yang, “A novel bagging C4.5 algorithm based on wrapper feature selection for supporting wise clinical decision making,” J. Biomed. Inform., vol. 78, no. November 2017, pp. 144–155, 2018, doi: 10.1016/j.jbi.2017.11.005

C. Rajeswari, B. Sathiyabhama, S. Devendiran, and K. Manivannan, “A Gear fault identification using wavelet transform, rough set based GA, ANN and C4.5 algorithm.,” Procedia Eng., vol. 97, pp. 1831–1841, 2014, doi: 10.1016/j.proeng.2014.12.337.

H. Bin Wang and Y. J. Gao, “Research on C4.5 algorithm improvement strategy based on MapReduce,” Procedia Comput. Sci., vol. 183, pp. 160–165, 2021, doi: 10.1016/j.procs.2021.02.045.

R. A. Awad, W. K. B. Khalil, and A. G. Attallah, “Feline panleukopenia viral infection in cats: Application of some molecular methods used for its diagnosis,” J. Genet. Eng. Biotechnol., vol. 16, no. 2, pp. 491–497, 2018, doi: 10.1016/j.jgeb.2018.08.001.

E. Brianti et al., “Treatment and long-term follow-up of a cat with leishmaniosis,” Parasites and Vectors, vol. 12, no. 1, pp. 1–7, 2019, doi: 10.1186/s13071-019-3388-9.

M. K. Chatzis et al., “Evaluation of clinicopathological abnormalities in sick cats naturally infected by Leishmania infantum,” Heliyon, vol. 6, no. 10, p. e05177, 2020, doi: 10.1016/j.heliyon.2020.e05177.

A. Pereira and C. Maia, “Leishmania infection in cats and feline leishmaniosis: An updated review with a proposal of a diagnosis algorithm and prevention guidelines,” Curr. Res. Parasitol. Vector-Borne Dis., vol. 1, no. April, p. 100035, 2021, doi: 10.1016/j.crpvbd.2021.100035.

X. Wang, C. Zhou, and X. Xu, “Application of C4.5 decision tree for scholarship evaluations,” Procedia Comput. Sci., vol. 151, no. 2018, pp. 179–184, 2019, doi: 10.1016/j.procs.2019.04.027.

A. Joshuva, R. S. Kumar, S. Sivakumar, G. Deenadayalan, and R. Vishnuvardhan, “An insight on VMD for diagnosing wind turbine blade faults using C4.5 as feature selection and discriminating through multilayer perceptron,” Alexandria Eng. J., vol. 59, no. 5, pp. 3863–3879, 2020, doi: 10.1016/j.aej.2020.06.041.

A. P. Muniyandi, R. Rajeswari, and R. Rajaram, “Network anomaly detection by cascading k-Means clustering and C4.5 decision tree algorithm,” Procedia Eng., vol. 30, no. 2011, pp. 174–182, 2012, doi: 10.1016/j.proeng.2012.01.849.

K. Bouchard, B. Bouchard, and A. Bouzouane, “A new qualitative spatial recognition model based on Egenhofer topological approach using C4.5 algorithm: Experiment and results,” Procedia Comput. Sci., vol. 5, pp. 497–504, 2011, doi: 10.1016/j.procs.2011.07.064.

Y. Xiang, K. Lu, S. L. James, T. B. Borlawsky, K. Huang, and P. R. O. Payne, “K-Neighborhood decentralization: A comprehensive solution to index the UMLS for large scale knowledge discovery,” J. Biomed. Inform., vol. 45, no. 2, pp. 323–336, 2012, doi: 10.1016/j.jbi.2011.11.012.

A. H. Khan and I. Porres, “Consistency of UML class, object and statechart diagrams using ontology reasoners,” J. Vis. Lang. Comput., vol. 26, pp. 42–65, 2015, doi: 10.1016/j.jvlc.2014.11.006.

I. D. Mienye, Y. Sun, and Z. Wang, “Prediction performance of improved decision tree-based algorithms: A review,” Procedia Manuf., vol. 35, pp. 698–703, 2019, doi: 10.1016/j.promfg.2019.06.011.

B. N. Lakshmi, T. S. Indumathi, and N. Ravi, “A Study on C.5 Decision Tree Classification Algorithm for Risk Predictions During Pregnancy,” Procedia Technol., vol. 24, pp. 1542–1549, 2016, doi: 10.1016/j.protcy.2016.05.128.

K. R. Pradeep and N. C. Naveen, “Lung Cancer Survivability Prediction based on Performance Using Classification Techniques of Support Vector Machines, C4.5 and Naive Bayes Algorithms for Healthcare Analytics,” Procedia Comput. Sci., vol. 132, pp. 412–420, 2018, doi: 10.1016/j.procs.2018.05.162.

V. L. Policicchio, A. Pietramala, and P. Rullo, “GAMoN: Discovering M-of-N {¬, ∨} hypotheses for text classification by a lattice-based Genetic Algorithm,” Artif. Intell., vol. 191–192, pp. 61–95, 2012, doi: 10.1016/j.artint.2012.07.003.

Y. Han, D. Zhao, and H. Hou, “Oil-immersed Transformer Internal Thermoelectric Potential Fault Diagnosis Based on Decision-tree of KNIME Platform,” Procedia Comput. Sci., vol. 83, no. Wtisg, pp. 1321–1326, 2016, doi: 10.1016/j.procs.2016.04.275.

Y. T. Guo et al., “DPI & DFI: A Malicious Behavior Detection Method Combining Deep Packet Inspection and Deep Flow Inspection,” Procedia Eng., vol. 174, pp. 1309–1314, 2017, doi: 10.1016/j.proeng.2017.01.276.

A. D. Boyd et al., “Physician nurse care: A new use of UMLS to measure professional contribution: Are we talking about the same patient a new graph matching algorithm?,” Int. J. Med. Inform., vol. 113, no. October 2017, pp. 63–71, 2018, doi: 10.1016/j.ijmedinf.2018.02.002.

R. C. Waldemarin and C. R. G. de Farias, “OBO to UML: Support for the development of conceptual models in the biomedical domain,” J. Biomed. Inform., vol. 80, no. November 2017, pp. 14–25, 2018, doi: 10.1016/j.jbi.2018.02.015.

J. Geller, Z. He, Y. Perl, C. P. Morrey, and J. Xu, “Rule-based support system for multiple UMLS semantic type assignments,” J. Biomed. Inform., vol. 46, no. 1, pp. 97–110, 2013, doi: 10.1016/j.jbi.2012.09.007.

S. Ain El Hayat, F. Toufik, and M. Bahaj, “UML/OCL based design and the transition towards temporal object relational database with bitemporal data,” J. King Saud Univ. - Comput. Inf. Sci., vol. 32, no. 4, pp. 398–407, 2020, doi: 10.1016/j.jksuci.2019.08.012.

M. N. N. Sitokdana, R. Tanone, and P. F. Tanaem, “Android-based digitalization of number system of traditional, Ngalum, Ketengban, Lepki and Arimtap tribes,” Procedia Comput. Sci., vol. 161, pp. 41–48, 2019, doi: 10.1016/j.procs.2019.11.097.

M. N. N. Sitokdana, R. Tanone, and P. F. Tanaem, “Digitalization of the local language dictionary of Pegunungan Bintang,” Procedia Comput. Sci., vol. 161, pp. 49–56, 2019, doi: 10.1016/j.procs.2019.11.098.

R. Hermawati and I. S. Sitanggang, “Web-Based Clustering Application Using Shiny Framework and DBSCAN Algorithm for Hotspots Data in Peatland in Sumatra,” Procedia Environ. Sci., vol. 33, pp. 317–323, 2016, doi: 10.1016/j.proenv.2016.03.082.




How to Cite

TAMSIR, N. (2021). ALGORITHM C4.5 IN CLASSIFYING HEALTH OF CAT. Journal of Information Technology and Its Utilization, 4(2), 56–63.