Selçuk, Yağmur SelenayGöktürk, Elvin Çoban2024-01-232024-01-232023979-835033752-5http://hdl.handle.net/10679/9069https://doi.org/10.1109/HORA58378.2023.10156805Providing 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.engrestrictedAccessconferenceObject10.1109/HORA58378.2023.10156805Correlation matrixData analysisHome healthcare serviceK-means clusteringMachine learningPre-processing2-s2.0-85165703805