Open AccessPrediction Of Medical Actions For Covid Patients Using Naïve Bayes Method
Author(s) -
Arfan Haqiqi,
Rais,
Istiqomah Dwi Andari,
Siti Fatmawati Fatimah
Publication year - 2021
Publication title -
elkom
Language(s) - English
Resource type - Journals
eISSN - 2714-5417
pISSN - 1907-0012
DOI - 10.51903/elkom.v14i2.464
Subject(s) - covid-19 , intensive care unit , medicine , asymptomatic , bayes' theorem , action (physics) , medical emergency , intensive care medicine , computer science , artificial intelligence , disease , bayesian probability , physics , quantum mechanics , infectious disease (medical specialty)
Management of medical actions carried out in handling patients who are ODP (people under monitoring), OTG (asymptomatic people), PDP (patient under monitoring) and positive Covid-19 patients is carried out based on assumptions, such as self-isolation, hospitalization, or special treatments in the ICU (Intensive Care Unit) room. The condition of the body in each patient is different, a patient may have same symptoms but the treatment is different, especially in elderly patients. Many problems occur in determining medical action because the patient's body condition is different. Therefore, it needs to be appointed as a research. The research method used in this study was Nive Bayes algorithm with supporting application Rapid Miner. It was applied to carry out the process of testing on patient data as much as 500 data, 25 variables or patient symptoms and 3 outputs as a form of medical action. Based on the results of the analysis carried out in this study, prediction of medical actions for ODP, PDP, OTG and positive Covid-19 patients were obtained by comparing training data with testing data using Rapid Miner application. It resulted that an accuracy rate of 76.00% was obtained