Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (1)

Search Parameters:
Authors = Julio Jerison Escudero Macrohon ORCID = 0000-0003-2738-4932

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
22 pages, 2628 KiB  
Article
COVID-19 Prediction Applying Supervised Machine Learning Algorithms with Comparative Analysis Using WEKA
by Charlyn Nayve Villavicencio, Julio Jerison Escudero Macrohon, Xavier Alphonse Inbaraj, Jyh-Horng Jeng and Jer-Guang Hsieh
Algorithms 2021, 14(7), 201; https://doi.org/10.3390/a14070201 - 30 Jun 2021
Cited by 53 | Viewed by 9557
Abstract
Early diagnosis is crucial to prevent the development of a disease that may cause danger to human lives. COVID-19, which is a contagious disease that has mutated into several variants, has become a global pandemic that demands to be diagnosed as soon as [...] Read more.
Early diagnosis is crucial to prevent the development of a disease that may cause danger to human lives. COVID-19, which is a contagious disease that has mutated into several variants, has become a global pandemic that demands to be diagnosed as soon as possible. With the use of technology, available information concerning COVID-19 increases each day, and extracting useful information from massive data can be done through data mining. In this study, authors utilized several supervised machine learning algorithms in building a model to analyze and predict the presence of COVID-19 using the COVID-19 Symptoms and Presence dataset from Kaggle. J48 Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes algorithms were applied through WEKA machine learning software. Each model’s performance was evaluated using 10-fold cross validation and compared according to major accuracy measures, correctly or incorrectly classified instances, kappa, mean absolute error, and time taken to build the model. The results show that Support Vector Machine using Pearson VII universal kernel outweighs other algorithms by attaining 98.81% accuracy and a mean absolute error of 0.012. Full article
Show Figures

Figure 1

Back to TopTop