A Comparison of Naive Bayes Methods, Logistic Regression and KNN for Predicting Healing of Covid-19 Patients in Indonesia

Romadhon, Manzilur Rahman and Kurniawan, Fachrul (2021) A Comparison of Naive Bayes Methods, Logistic Regression and KNN for Predicting Healing of Covid-19 Patients in Indonesia. Presented at 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT), 9-11 April 2021, Surabaya, Indonesia.

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Abstract

Since it was declared a global pandemic by the World Health Organization (WHO), the number of cases of Covid-19 patients who died has continued to increase. One of the countries with the highest death rate in the world is Indonesia. On Saturday, April 4, 2020, Indonesia reached the highest death
rate for Covid-19 patients, around 9.11%. This number must be suppressed so that there are no more victims. For this reason, it is necessary to know actually the factors that can reduce the risk of death and predict the chance of curing Covid-19 patients. In data mining, there are several methods that can be used to predict a patient's recovery rate by considering several variables. The variables used in this study were age and gender. Naive Bayes Method, logistic regression, and K-Nearest Neighbor (KNN) are the methods to be chosen in this study to analyze their most accurate performance. The result shows that KNN has the highest accuracy, which is 0.750 compared to logistic regression which has a value of 0.703 as well as Naive Bayes which has the same value. Meanwhile, the level of precision of the three models shows that KNN also has the highest value, namely 0.750 than logistic regression and Naive Bayes which have the same value, namely 0.700. The recall value of the three also shows that the kNN remains the highest with 0.750 compared to the two comparison models which have the same value, namely 0.708

Item Type:	Conference (Paper)
Keywords:	KNN, Naive Bayes, logistic regression, covid-19
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
Divisions:	Graduate Schools > Magister Programme > Graduate School of Informatics Engineering
Depositing User:	manzilur rahman romadhon
Date Deposited:	23 Jul 2021 18:13

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