Matches in SemOpenAlex for { <https://semopenalex.org/work/W4321488891> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W4321488891 abstract "One of the most avoidable environmental public health problems is childhood lead exposure, which can lead to a variety of disorders such as reduced muscle coordination, stunted bone and muscle growth, damaged nervous system, impaired speech and language, and seizures. It is difficult to predict whether someone will be exposed to lead, but studies have found a correlation between lead exposure and things like household income, ethnicity or refugee status, reliance on Medicaid, older homes built before 1978 with lead paint in poor condition, proximity to industry, and people working in lead-exposed environments like manufacturing, repair, welding, or renovation jobs. We predict potential lead exposure at the zip code level using data from Massachusetts and New York's Blood Lead Levels that are publicly available. Additionally, using data gathered from news stories and other media, we offer a sentimental analysis approach based on Long Short Term Memory (LSTM) deep learning algorithm to assess how well the lead programs are being implemented in these states. Using six different machine learning algorithms, we achieved the best performance with LightGBM, with an accuracy of 83.6 percent for New York and 89.1 percent for Massachusetts and an f1-score of 0.81 and 0.83, respectively. Through the average sentimental analysis ratings for New York and Massachusetts, we found a strong correlation with elevated blood lead levels reported within the states. When compared to earlier researchers' calculations of AUC ROC scores at the individual level, our methodology yields greater AUC ROC scores." @default.
- W4321488891 created "2023-02-23" @default.
- W4321488891 creator A5014369148 @default.
- W4321488891 creator A5041928943 @default.
- W4321488891 creator A5072307876 @default.
- W4321488891 date "2023-02-07" @default.
- W4321488891 modified "2023-10-14" @default.
- W4321488891 title "Machine Learning and Sentiment Analysis for Predicting Environmental Lead Toxicity in Children at the ZIP Code Level" @default.
- W4321488891 cites W1972787195 @default.
- W4321488891 cites W2036749867 @default.
- W4321488891 cites W2054071000 @default.
- W4321488891 cites W2080845032 @default.
- W4321488891 cites W2133341045 @default.
- W4321488891 cites W2334478138 @default.
- W4321488891 cites W2922073769 @default.
- W4321488891 cites W2962844373 @default.
- W4321488891 cites W3087059313 @default.
- W4321488891 cites W3094227359 @default.
- W4321488891 cites W3206226883 @default.
- W4321488891 cites W4205184193 @default.
- W4321488891 cites W4313452814 @default.
- W4321488891 doi "https://doi.org/10.1109/icaic57335.2023.10044177" @default.
- W4321488891 hasPublicationYear "2023" @default.
- W4321488891 type Work @default.
- W4321488891 citedByCount "0" @default.
- W4321488891 crossrefType "proceedings-article" @default.
- W4321488891 hasAuthorship W4321488891A5014369148 @default.
- W4321488891 hasAuthorship W4321488891A5041928943 @default.
- W4321488891 hasAuthorship W4321488891A5072307876 @default.
- W4321488891 hasConcept C114793014 @default.
- W4321488891 hasConcept C118552586 @default.
- W4321488891 hasConcept C119857082 @default.
- W4321488891 hasConcept C127313418 @default.
- W4321488891 hasConcept C154945302 @default.
- W4321488891 hasConcept C15744967 @default.
- W4321488891 hasConcept C160735492 @default.
- W4321488891 hasConcept C17744445 @default.
- W4321488891 hasConcept C199539241 @default.
- W4321488891 hasConcept C2776534028 @default.
- W4321488891 hasConcept C2777093003 @default.
- W4321488891 hasConcept C2781037878 @default.
- W4321488891 hasConcept C3018590553 @default.
- W4321488891 hasConcept C3020026551 @default.
- W4321488891 hasConcept C41008148 @default.
- W4321488891 hasConcept C71924100 @default.
- W4321488891 hasConcept C76155785 @default.
- W4321488891 hasConcept C77088390 @default.
- W4321488891 hasConcept C82876162 @default.
- W4321488891 hasConcept C99454951 @default.
- W4321488891 hasConceptScore W4321488891C114793014 @default.
- W4321488891 hasConceptScore W4321488891C118552586 @default.
- W4321488891 hasConceptScore W4321488891C119857082 @default.
- W4321488891 hasConceptScore W4321488891C127313418 @default.
- W4321488891 hasConceptScore W4321488891C154945302 @default.
- W4321488891 hasConceptScore W4321488891C15744967 @default.
- W4321488891 hasConceptScore W4321488891C160735492 @default.
- W4321488891 hasConceptScore W4321488891C17744445 @default.
- W4321488891 hasConceptScore W4321488891C199539241 @default.
- W4321488891 hasConceptScore W4321488891C2776534028 @default.
- W4321488891 hasConceptScore W4321488891C2777093003 @default.
- W4321488891 hasConceptScore W4321488891C2781037878 @default.
- W4321488891 hasConceptScore W4321488891C3018590553 @default.
- W4321488891 hasConceptScore W4321488891C3020026551 @default.
- W4321488891 hasConceptScore W4321488891C41008148 @default.
- W4321488891 hasConceptScore W4321488891C71924100 @default.
- W4321488891 hasConceptScore W4321488891C76155785 @default.
- W4321488891 hasConceptScore W4321488891C77088390 @default.
- W4321488891 hasConceptScore W4321488891C82876162 @default.
- W4321488891 hasConceptScore W4321488891C99454951 @default.
- W4321488891 hasLocation W43214888911 @default.
- W4321488891 hasOpenAccess W4321488891 @default.
- W4321488891 hasPrimaryLocation W43214888911 @default.
- W4321488891 hasRelatedWork W125261028 @default.
- W4321488891 hasRelatedWork W188652988 @default.
- W4321488891 hasRelatedWork W2031295744 @default.
- W4321488891 hasRelatedWork W2120348216 @default.
- W4321488891 hasRelatedWork W2401457046 @default.
- W4321488891 hasRelatedWork W2602767582 @default.
- W4321488891 hasRelatedWork W2793798142 @default.
- W4321488891 hasRelatedWork W4311097295 @default.
- W4321488891 hasRelatedWork W825627675 @default.
- W4321488891 hasRelatedWork W2887972580 @default.
- W4321488891 isParatext "false" @default.
- W4321488891 isRetracted "false" @default.
- W4321488891 workType "article" @default.