Comparative Analysis of Machine Learning in Predicting the Treatment Status of COVID-19 Patients

Abstract

COVID-19 has become a global pandemic that causes many deaths, so medical treatment for COVID-19 patients gets special attention, whether hospitalized or self-isolated. However, the problem in medical action is not easy, and the most frequent mistakes are due to inaccuracies in medical decision-making. Meanwhile, machine learning can predict with high accuracy. For that, or that's why this study aims to propose a data mining classification method as a machine learning model to predict the treatment status of COVID-19 patients accurately, whether hospitalized or self-isolated. The data mining method used in this research is the Random Forest (RF) and Support Vector Machine (SVM) algorithm with Confusion Matrix and k-fold Cross Validation testing. The finding indicated that the machine learning model has an accuracy of up to 94% with the RF algorithm and up to 92% with the SVM algorithm in predicting the COVID-19 patient's treatment status. It means that the machine learning model using the RF algorithm has more accurate accuracy than the SVM algorithm in predicting or recommending the treatment status of COVID-19 patients. The implication is that RF machine learning can help/replace the role of medical experts in predicting the patient's care status.

Preferences for Hybrid Learning of Postgraduate Students

“…Hybrid learning is a form of innovative education that combines two learning models: face-to-face learning and online learning (Anggrawan et al, 2021). The hybrid learning model is inspired by project-based learning (Liang, 2021), where students are encouraged to explore their environment rather than solely relying on traditional classroom-based face-to-face learning.…”

The Impact of Learning Strategies (Values Clarification Technique and Social Science Inquiry) and Students' Cognitive Style on Tolerance

“…They are likelier to perform well in numerical, natural science and problem-solving tasks. Many studies related to the two types of cognitive styles FD and Fl have been carried out, including: (Amin et al, 2023;Anggrawan, Nuraini, et al, 2021;Batubara, 2023;Bonavita et al, 2023;Chang & Yang, 2023;Cheng, 2023;Dasen, 2022;Kafipour & Khoshnood, 2023;Tambi et al, 2021;Zubaidah R et al, 2023). Based on research conducted by researchers, there are generally different variations in learning outcomes between individuals with the FD cognitive style and individuals with the Fl cognitive style.…”

“…Students who fall into the FD group or FI are determined based on the total score obtained. If the total score is in the range 0-8 then the student is categorized in the FD group and if the total score is in the range 10-18 then the student is categorized in the FI group, while students who get a score of 9 are categorized as neutral(Anggrawan, Nuraini, et al, 2021;Forsyth & Rizert, 2018). In this research activity, research subjects who placed cognitive styles in the neutral category were not considered in data analysis.Instruments for tolerance of students were developed in the form of pretests and posttests with rating scales with a total of fifteen (15) questions.…”

Developing Augmented Reality Learning and Measuring Its Effect on Independent Learning Compared to Traditional Learning

“…Besides that, technological developments have changed many areas of life by manifesting in different directions, including optimizing teaching and learning [4]. Technological advances are the concern of researchers to realize a technology-based education process [5], [6], [7], [8]. Students today leave the old way of learning in the educational field by turning to computers and internet digital educational media [9], [5].…”