Prediction, classification and recommendation in e-health via contextual partitioning

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buir.contributor.authorQureshi, Muhammad Anjum
buir.contributor.orcidQureshi, Muhammad Anjum|0000-0001-6426-1267
dc.citation.epage4en_US
dc.citation.spage1en_US
dc.contributor.authorQureshi, Muhammad Anjum
dc.coverage.spatialIstanbul, Turkeyen_US
dc.date.accessioned2022-02-01T11:14:27Z
dc.date.available2022-02-01T11:14:27Z
dc.date.issued2021-07-19
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionConference Name: 2021 29th Signal Processing and Communications Applications Conference (SIU)en_US
dc.descriptionDate of Conference: 9-11 June 2021en_US
dc.description.abstractIn this paper, we propose a multipurpose contextual partitioning based estimation algorithm. Exploiting the similarities between contexts (side information: such as age, Gender etc.,) related to patient data in healthcare repository or database, multidimensional spheres are generated over Euclidean space. Then, conditional first and second order characteristics are predicted using sample-based mean and covariance. These conditional statistics of particular patient data subset (sphere) serve the following purposes: i) Prediction for missing values (conditional mean), ii) Partitioned principal components for better classification (conditional covariance) and iii) Recommendation for medical Test or physician (conditional covariance). The proposed approach uniformly partitions the context space into spheres, and then, for each sphere estimates the conditional mean and covariance using only the data (excluding the context data) in the selected sphere. Hence, providing three in one solution i.e., Prediction, Classification and Recommendation for healthcare data using conditional probabilistic characteristics. The overall error is decomposed into estimation and approximation errors. In a particular sphere, estimation error is dependent on the number of instances, while approximation error is dependent on the dissimilarity of instances.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2022-02-01T11:14:27Z No. of bitstreams: 1 Prediction_Classification_and_Recommendation_in_e-Health_via_Contextual_Partitioning.pdf: 1055517 bytes, checksum: 82ad5a348d93004a691821444768bfef (MD5)en
dc.description.provenanceMade available in DSpace on 2022-02-01T11:14:27Z (GMT). No. of bitstreams: 1 Prediction_Classification_and_Recommendation_in_e-Health_via_Contextual_Partitioning.pdf: 1055517 bytes, checksum: 82ad5a348d93004a691821444768bfef (MD5) Previous issue date: 2021-07-19en
dc.identifier.doi10.1109/SIU53274.2021.9477921en_US
dc.identifier.eisbn978-1-6654-3649-6
dc.identifier.isbn978-1-6654-3650-2
dc.identifier.issn2165-0608
dc.identifier.urihttp://hdl.handle.net/11693/76939
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/SIU53274.2021.9477921en_US
dc.source.titleIEEE Signal Processing and Communications Applications (SIU)en_US
dc.subjectPredictionen_US
dc.subjectClassificationen_US
dc.subjectRecommendationen_US
dc.subjectUniform partitionen_US
dc.titlePrediction, classification and recommendation in e-health via contextual partitioningen_US
dc.typeConference Paperen_US

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