Logistic Regression Using Hyperparameter Optimization on COVID-19 Patients’ Vital Status

Logistic Regression Using Hyperparameter Optimization on COVID-19 Patients’ Vital Status

logistic regression; hyperparameter optimization; covid-19; patients status

This study aims to classify COVID-19 patients based on the results of their hematology tests. Hematology test results have been
shown to be useful in identifying the severity and risk of COVID-19 patients. Specifically, this study focuses on classifying
COVID-19 patients based on their vital status, namely Deceased and Alive. The dataset used in this study contains four
variables: white blood cells (WBC), neutrophils (NEU), lymphocytes (LYM), and Neutrophil Lymphocyte Ratio (NLR). Logistic
Regression algorithm was used to solve the problem, and hyperparameter optimization was implemented to obtain the best
model performance. The objective of this study was to build the best parameter in classifying the patients’ vital status. The
proposed model achieved an accuracy score of 78%, which is the best performance among the tested models. The results of
this study provide a key component for decision making in hospitals, as it provides a way to quickly and accurately identify the
vital status of COVID-19 patients. This study has important implications for managing the COVID-19 pandemic and should
be of interest to researchers and practitioners in the field.

Vinna Rahmayanti Setyaning Nastiti, Yufis Azhar, Riska Septiana Putri

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

June 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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