Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes
Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes
Abstract
Malaria is a disease caused by the Plasmodium parasite that transmitted by female Anopheles mosquitoes. Malaria can become a dangerous disease if late have the medical treatment. The late medical treatment happened because of misdiagnosis and lack of medical staff, especially in the countryside. This problem can cause severe malaria that has complications. This study creates a system prediction to classify the severe malaria disease using Classification and Regression Tree (CART) method and the probability of malaria complication using Naïve Bayes method. The first step of this study is classifying the patients that have symptom are infected severe malaria or not based on the model that has been built. The next step, if the patient classified severe malaria then the data predicted if there any probability of complication by the malaria. There are 8 possibilities of complication malaria which are convulsion, hypoglycemia, hyperpyrexia, and the combinations of these four. The first step will evaluate by using F-score, precision and recall while the second step will evaluate by using accuracy. The highest result F-score, precision and recall are 0.551, 0.471 and 0.717. The highest accuracy 81.2% which predicted the complication is Hypoglycemia.
Downloads
References
References
Biantong, T.R., Furqon, M.T. and Soebroto, A.A., 2018. Implementasi Metode Support Vector Machine Untuk Klasifikasi Jenis Penyakit Malaria. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 2548, p.964X.
World Health Organization, 2015. Guidelines for the treatment of malaria. World Health Organization.
Kurniawan, R. ed., 2019. Profil kesehatan Indonesia tahun 2018. Kementerian Kesehatan RI.
Hamisu, I.A., 2019. Malaria Prediction using Bayesian and other Machine Learning Techniques (Doctoral dissertation).
Parveen, R., Jalbani, A.H., Shaikh, M., Memon, K.H., Siraj, S., Nabi, M. and Lakho, S., 2017. Prediction of Malaria using Artificial Neural Network. IJCSNS, 17(12), p.79.
Parveen, R., Song, W., Qiu, B., Bhatti, M.N., Hassan, T. and Liu, Z., 2021. Probabilistic Model-Based Malaria Disease Recognition System. Complexity, 2021.
Shofia, E.N., Putri, R.R.M. and Arwan, A., 2017. Sistem Pakar Diagnosis Penyakit Demam: DBD, Malaria dan Tifoid Menggunakan Metode K-Nearest Neighbor–Certainty Factor. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, e-ISSN.
Sujith, C., Ramya, G., Sree, B.L.J., Teja, G. and Nagamani, T., MALARIA DETECTION USING CONVOLUTIONAL NEURAL NETWORK.
Fuhad, K.M., Tuba, J.F., Sarker, M., Ali, R., Momen, S., Mohammed, N. and Rahman, T., 2020. Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and Its Smartphone Based Application. Diagnostics, 10(5), p.329.
Sharma, V., Kumar, A., Lakshmi Panat, D. and Karajkhede, G., 2015. Malaria outbreak prediction model using machine learning. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 4(12).
Buczak, A.L., Baugher, B., Guven, E., Ramac-Thomas, L.C., Elbert, Y., Babin, S.M. and Lewis, S.H., 2015. Fuzzy association rule mining and classification for the prediction of malaria in South Korea. BMC medical informatics and decision making, 15(1), p.47.
Ford, C.T. and Janies, D., 2020. Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria. F1000Research, 9(62), p.62.
Prasetiyowati, S.S. and Sibaroni, Y., 2018. Prediction of DHF disease spreading patterns using inverse distances weighted (IDW), ordinary and universal kriging. JPhCS, 971(1), p.012010.
Adeboye, N.O., Abimbola, O.V. and Folorunso, S.O., 2020. Malaria patients in Nigeria: Data exploration approach. Data in brief, 28, p.104997.
Chakrabarti, S., Cox, E., Frank, E., Güting, R.H., Han, J., Jiang, X., Kamber, M., Lightstone, S.S., Nadeau, T.P., Neapolitan, R.E. and Pyle, D., 2008. Data mining: know it all. Morgan Kaufmann.
Roiger, R.J., 2017. Data mining: a tutorial-based primer. CRC press.
Ma, X., 2018. Using classification and regression trees: A practical primer. IAP.
Timofeev, R., 2004. Classification and regression trees (CART) theory and applications. Humboldt University, Berlin, pp.1-40.
Han, J., Pei, J. and Kamber, M., 2011. Data mining: concepts and techniques. Elsevier.
EMC Education Services, 2015. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. Wiley.
Sammut, C. and Webb, G.I. eds., 2011. Encyclopedia of machine learning. Springer Science & Business Media.
Torres-Moreno, J.M. ed., 2014. Automatic text summarization. John Wiley & Sons.
Brownlee, J., 2020. Imbalanced Classification with Python: Better Metrics, Balance Skewed Classes, Cost-Sensitive Learning. Machine Learning Mastery
Copyright (c) 2021 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
