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

  • Rachmadania Irmanita Telkom University
  • Sri Suryani Prasetiyowati
  • Yuliant Sibaroni
Keywords: malaria, classification and regression tree, naive bayes


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.


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How to Cite
Irmanita, R., Sri Suryani Prasetiyowati, & Yuliant Sibaroni. (2021). Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(1), 10 - 16.
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