Prediction of Survival COVID-19 Patients by Using Backpropagation Neural Network Algorithm

COVID-19 is a disease caused by a virus and increasing in cases every day. This is because the large number of patients makes it difficult to be treated at the hospital. This is behind the need for survival prediction of COVID-19 patients within 48 days so that the medical team can prioritize patients who are predicted to not survive on that period. In this research, the firefly algorithm is used which aims to select attributes and will perform comparisons for data that is balance or imbalance and combined with data that do feature selection and does not feature selection. The data that will be used are age, asthma, diabetes, gender, COPD, pregnancy, hypertension, obesity, ICU, chronic kidney disease, smoking, heart disease, immune deficiency, pneumonia, and other medical history. In this research, the selected attributes were gender, type of patient, intubation, pneumonia, age, pregnancy, diabetes, COPD (Chronic Obstructive Pulmonary Disease), asthma, hypertension, other diseases, obesity, chronic kidney disease, smokers, contact with COVID patients, and ICU. The prediction model with the highest level of performance is a model with balanced data with a recall value of 0.79, then a precision value of 0.93, then an f score of 0.85, then an accuracy value of 0.86, then a specificity 0,93, then a NPV 0,82 and a geometric mean value of 0.87