Matches in SemOpenAlex for { <https://semopenalex.org/work/W4367011964> ?p ?o ?g. }
Showing items 1 to 71 of
71
with 100 items per page.
- W4367011964 endingPage "442" @default.
- W4367011964 startingPage "431" @default.
- W4367011964 abstract "HealthCare Data Analytics is the analysis technique using current and historical data related to the health domain to improve outreach, predict trends, and manage health-related matters. Severity level prediction will help battle the COVID-19 pandemic by providing early predictions for the treatment. Features are very significant for classification, so here this research is proposing to improve the model performance with the handcrafted features like severity level and weather category. Supervised machine learning models like random forest, decision tree, AdaBoost, K-Nearest Neighbors, and naïve Bayes analyzed the epidemiological dataset of COVID-19 to predict the severity level of COVID-19-positive patients. Various sampling techniques and feature selection techniques like feature score, feature importance, and correlation matrix are used to minimize the execution time and to improve and fine-tune the model. The result of the performance evaluation measure of the machine learning models showed that the random forest classifier has the best results of accuracy as 98.32% and a precision value as 97.52% followed by the decision tree classifier with SMOTE over-sampling technique when used the handcrafted features." @default.
- W4367011964 created "2023-04-27" @default.
- W4367011964 creator A5045416800 @default.
- W4367011964 creator A5065416217 @default.
- W4367011964 date "2023-01-01" @default.
- W4367011964 modified "2023-09-25" @default.
- W4367011964 title "Fine-Tuned Predictive Models for Forecasting Severity Level of COVID-19 Patient Using Epidemiological Data" @default.
- W4367011964 cites W2778170490 @default.
- W4367011964 cites W2938285755 @default.
- W4367011964 cites W3038780555 @default.
- W4367011964 cites W3040299034 @default.
- W4367011964 cites W3080568059 @default.
- W4367011964 cites W3089460656 @default.
- W4367011964 cites W3105951585 @default.
- W4367011964 cites W3106763172 @default.
- W4367011964 cites W3162351260 @default.
- W4367011964 cites W4206233629 @default.
- W4367011964 cites W4210969603 @default.
- W4367011964 doi "https://doi.org/10.1007/978-981-19-5191-6_35" @default.
- W4367011964 hasPublicationYear "2023" @default.
- W4367011964 type Work @default.
- W4367011964 citedByCount "0" @default.
- W4367011964 crossrefType "book-chapter" @default.
- W4367011964 hasAuthorship W4367011964A5045416800 @default.
- W4367011964 hasAuthorship W4367011964A5065416217 @default.
- W4367011964 hasConcept C119857082 @default.
- W4367011964 hasConcept C12267149 @default.
- W4367011964 hasConcept C124101348 @default.
- W4367011964 hasConcept C141404830 @default.
- W4367011964 hasConcept C148483581 @default.
- W4367011964 hasConcept C154945302 @default.
- W4367011964 hasConcept C169258074 @default.
- W4367011964 hasConcept C41008148 @default.
- W4367011964 hasConcept C45942800 @default.
- W4367011964 hasConcept C52001869 @default.
- W4367011964 hasConcept C75684735 @default.
- W4367011964 hasConcept C83209312 @default.
- W4367011964 hasConcept C84525736 @default.
- W4367011964 hasConcept C95623464 @default.
- W4367011964 hasConceptScore W4367011964C119857082 @default.
- W4367011964 hasConceptScore W4367011964C12267149 @default.
- W4367011964 hasConceptScore W4367011964C124101348 @default.
- W4367011964 hasConceptScore W4367011964C141404830 @default.
- W4367011964 hasConceptScore W4367011964C148483581 @default.
- W4367011964 hasConceptScore W4367011964C154945302 @default.
- W4367011964 hasConceptScore W4367011964C169258074 @default.
- W4367011964 hasConceptScore W4367011964C41008148 @default.
- W4367011964 hasConceptScore W4367011964C45942800 @default.
- W4367011964 hasConceptScore W4367011964C52001869 @default.
- W4367011964 hasConceptScore W4367011964C75684735 @default.
- W4367011964 hasConceptScore W4367011964C83209312 @default.
- W4367011964 hasConceptScore W4367011964C84525736 @default.
- W4367011964 hasConceptScore W4367011964C95623464 @default.
- W4367011964 hasLocation W43670119641 @default.
- W4367011964 hasOpenAccess W4367011964 @default.
- W4367011964 hasPrimaryLocation W43670119641 @default.
- W4367011964 hasRelatedWork W2904660175 @default.
- W4367011964 hasRelatedWork W2911198546 @default.
- W4367011964 hasRelatedWork W3170784702 @default.
- W4367011964 hasRelatedWork W3194296141 @default.
- W4367011964 hasRelatedWork W3204641204 @default.
- W4367011964 hasRelatedWork W4293069612 @default.
- W4367011964 hasRelatedWork W4294976306 @default.
- W4367011964 hasRelatedWork W4313463492 @default.
- W4367011964 hasRelatedWork W4362588981 @default.
- W4367011964 hasRelatedWork W4375930479 @default.
- W4367011964 isParatext "false" @default.
- W4367011964 isRetracted "false" @default.
- W4367011964 workType "book-chapter" @default.