## 5 Conclusions  
This study aimed to build a COVID-19 presence predictor model by applying five supervised machine learning algorithms, including J48 Decision Tree, Random Forest, K-Nearest Neighbors, Naive Bayes and Support Vector Machine. A comparative analysis was conducted by evaluating the model's performance in 10-fold cross-validation through WEKA machine learning software. The results show that the support vector machine using the Pearson VII universal kernel is the best machine learning algorithm, having a 98.81% accuracy, and 0.012 mean absolute error. The Support Vector Machine algorithm outperformed other algorithms in terms of accuracy, precision, recall, F-Measure, correctly and incorrectly classified instances, kappa statistic score, mean absolute error, andtime taken to build the model. Moreover, the findings also show that Random Forest is the second best algorithm to be considered in building a COVID-19 presence predictor, because it has the same accuracy measures as those achieved by the Support Vector Machine algorithm, except for the mean absolute error. The Random Forest algorithm can be considered in developing a high-performing model with a faster training time as compared with the Support Vector Machine. In addition, K-Nearest Neighbors is the third most appropriate algorithm to use in terms of accuracy measures, because it can also build a model in a short period of time compared to other algorithms. Then, the J48 Decision Tree is ranked as the fourth, and Naive Bayes is ranked as the fifth most appropriate algorithms to be considered. This study can serve as a decision support system for medical practitioners, using the developed model as an aide in detecting COVID-19 presence in a person based on the declared symptoms. Additionally, individuals who are experiencing some COVID-19 related symptoms can use it to determine the possibility of being positive or negative in COVID-19 testing. This study can encourage individuals to seek immediate consultation with the doctor, and promotes the early diagnosis of the disease. In this way, it can help to prevent the spread of this contagious disease, diminishing the danger to human lives. The developed model in this study can be utilized to build an application with the following advantages:  
* Individuals can easily check the possibility of acquiring COVID-19 based on symptoms;
* This study can be used as a preliminary patient assessment for medical practitioners;
* Helping businesses to restrict physical contact with customers possibly having COVID-19;
* This study can serve as an additional self-management tool for quarantine facilities to monitor if the person have developed COVID-19 symptoms while in isolation;
* The community and government can use this study as a tool to reduce the spread of the virus through early detection of COVID-19. Studies involving additional data or symptoms from hospital records, individuals who acquired the virus, COVID-19 survivors, patients under investigation, or management can also be considered. A model that can detect the potential severity of COVID-19 can also be developed to provide additional information regarding necessary steps and possible treatments to consider. Conceptualization, C.N.V. and J.J.E.M. ; methodology, C.N.V. and J.J.E.M. ; validation, J.J.E.M. and X.A.I. ; formal analysis, C.N.V. and J.J.E.M. ; writing--original draft preparation, C.N.V. ; writing--review and editing, J.J.E.M., X.A.I., and J.-H.J. ; visualization, C.N.V. ; supervision, [PERSON]-H.J. and J.-G.H. ; project administration, J.-H.J. and [PERSON]-G.H. All authors have read and agreed to the published version of the manuscript. This research was funded by Bulacan State University as an International Faculty Scholar and I-Shou University together with Taiwan Ministry of Education (MOE) as MOE New Southbound Elite Scholar. Ethical review and approval were waived for this study, because the aim of this study is to analyze symptoms available in a public dataset to determine if there is a possibility of the presence COVID-19 or not.