Andrei James Agbuya, Risty Acerado, Roselia Morco, Ricaela Glipo, Alliah Clara Santos
{"title":"Predictive Analytics for the Coronavirus Recovery Rate in the Philippines","authors":"Andrei James Agbuya, Risty Acerado, Roselia Morco, Ricaela Glipo, Alliah Clara Santos","doi":"10.1145/3520084.3520115","DOIUrl":null,"url":null,"abstract":"The Philippines is one of the countries where the coronavirus has spread. The virus has infected almost every Filipino individual; coronavirus affects people of all ages, from children to adults, and as a result, recovery rate is unknown. This research aims to develop a predictive model using random forest algorithms to predict the high and low recovery rate by age. Based on the descriptive analysis of the data set, the age range of 20 to 29 has a 99.3 percent recovery rate compared to other age groups. The Random Forest Predictive Model was able to predict the high recovery rate with an accuracy rate of 93%.","PeriodicalId":444957,"journal":{"name":"Proceedings of the 2022 5th International Conference on Software Engineering and Information Management","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 5th International Conference on Software Engineering and Information Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3520084.3520115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
The Philippines is one of the countries where the coronavirus has spread. The virus has infected almost every Filipino individual; coronavirus affects people of all ages, from children to adults, and as a result, recovery rate is unknown. This research aims to develop a predictive model using random forest algorithms to predict the high and low recovery rate by age. Based on the descriptive analysis of the data set, the age range of 20 to 29 has a 99.3 percent recovery rate compared to other age groups. The Random Forest Predictive Model was able to predict the high recovery rate with an accuracy rate of 93%.
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