Advancing home healthcare through machine learning: Predicting service time for enhanced patient care
dc.contributor.author | Selçuk, Yağmur Selenay | |
dc.contributor.author | Göktürk, Elvin Çoban | |
dc.date.accessioned | 2024-01-23T12:12:46Z | |
dc.date.available | 2024-01-23T12:12:46Z | |
dc.date.issued | 2023 | |
dc.identifier.isbn | 979-835033752-5 | |
dc.identifier.uri | http://hdl.handle.net/10679/9069 | |
dc.identifier.uri | https://ieeexplore.ieee.org/document/10156805 | |
dc.description.abstract | Providing healthcare services at home is crucial for patients who require long-term care or face mobility or other health-related constraints that prevent them from traveling to healthcare facilities. Effective data analysis techniques are needed to optimize these services to understand patient needs and allocate resources efficiently. Machine learning algorithms can analyze big datasets generated from home healthcare services to identify patterns, trends, and predictive factors. By utilizing these techniques, predictive models for service time can be developed, leading to improved patient outcomes, increased efficiency, and reduced costs. This study explores the significance of various features in predicting service time for home healthcare services by analyzing real-life data using data analysis techniques. By developing a correlation matrix, healthcare providers can examine the relationships between features as well as their connections with the target value, thereby providing valuable managerial insights into improving the quality of home healthcare services through enhanced predictions of service time. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) | |
dc.rights | restrictedAccess | |
dc.title | Advancing home healthcare through machine learning: Predicting service time for enhanced patient care | en_US |
dc.type | Conference paper | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.authorID | (ORCID 0000-0002-3818-1957 & YÖK ID 125199) Çoban, Elvin | |
dc.contributor.ozuauthor | Göktürk, Elvin Çoban | |
dc.identifier.doi | 10.1109/HORA58378.2023.10156805 | en_US |
dc.subject.keywords | Correlation matrix | en_US |
dc.subject.keywords | Data analysis | en_US |
dc.subject.keywords | Home healthcare service | en_US |
dc.subject.keywords | K-means clustering | en_US |
dc.subject.keywords | Machine learning | en_US |
dc.subject.keywords | Pre-processing | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85165703805 | |
dc.contributor.ozugradstudent | Selçuk, Yağmur Selenay | |
dc.relation.publicationcategory | Conference Paper - International - Institutional Academic Staff and PhD Student |
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |
This item appears in the following Collection(s)
Share this page