{"id":13373,"date":"2023-08-09T16:04:37","date_gmt":"2023-08-09T14:04:37","guid":{"rendered":"https:\/\/sano.science\/?post_type=seminars&#038;p=13373"},"modified":"2023-08-24T16:13:15","modified_gmt":"2023-08-24T14:13:15","slug":"sanda-a-small-and-incomplete-dataset-analyser","status":"publish","type":"seminars","link":"https:\/\/sano.science\/seminars\/sanda-a-small-and-incomplete-dataset-analyser\/","title":{"rendered":"91. SaNDA: a Small and iNcomplete Dataset Analyser"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\">Abstract<\/h2>\n\n\n\n<p class=\" eplus-wrapper\">In personalised health, small datasets with missing data are quite common. Current Machine Learning methods are unable to process such datasets in a meaningful way due to the huge data volume requirement. To address this problem, we proposed a new Small and iNcomplete Dataset Analyser (SaNDA) to process such datasets in a meaningful way. Due to the characteristics of these datasets and the criticality of the domain, an explainable method was mandatory. Thus, SaNDA prioritised explainability over efficiency. We evaluated our proposal against the current standard of explainable methods: Random Forests. We observed that our proposal outperforms Random Forest when there is more missing data and\/or lower number of entries in the dataset, obtaining less favourable results over typically larger, well-curated datasets. Given the difficulties in obtaining complete, reliable data in the healthcare field, we consider that our proposal could be useful for practitioners.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading eplus-wrapper\">About the author<\/h2>\n\n\n\n<p class=\" eplus-wrapper\"><strong>Alfredo Ibias<\/strong>&nbsp;received B.A. degrees in Computer Science and in Mathematics from Complutense University of Madrid, Spain; an M.A. degree in Formal Methods in Computer Science from the same university; and a Ph.D. degree in Computer Science at the same university too. He is currently working at Sano as a Postdoctoral Researcher focused on developing AI methods for healthcare.<\/p>\n\n\n\n<p class=\" eplus-wrapper\"><a href=\"http:\/\/seminar.sano.science\/\"><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Alfredo Ibias, Personal Health Data Science Team, Sano Centre for Computational Medicine, Krakow, PL<\/p>\n","protected":false},"featured_media":13374,"template":"","class_list":["post-13373","seminars","type-seminars","status-publish","has-post-thumbnail","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v27.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>91. SaNDA: a Small and iNcomplete Dataset Analyser - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/seminars\/sanda-a-small-and-incomplete-dataset-analyser\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"91. 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SaNDA: a Small and iNcomplete Dataset Analyser"}]},{"@type":"WebSite","@id":"https:\/\/sano.science\/#website","url":"https:\/\/sano.science\/","name":"Centre for Computational Personalized Medicine","description":"Sano \u2013 Centre for Computational Medicine","publisher":{"@id":"https:\/\/sano.science\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sano.science\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/sano.science\/#organization","name":"Sano \u2013 Centre for Computational Medicine","alternateName":"Sano","url":"https:\/\/sano.science\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/sano.science\/#\/schema\/logo\/image\/","url":"https:\/\/sano.science\/wp-content\/uploads\/2024\/05\/logo_sano_podstawowe.png","contentUrl":"https:\/\/sano.science\/wp-content\/uploads\/2024\/05\/logo_sano_podstawowe.png","width":700,"height":265,"caption":"Sano \u2013 Centre for Computational Medicine"},"image":{"@id":"https:\/\/sano.science\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/sano.science\/","https:\/\/x.com\/sanoscience","https:\/\/www.linkedin.com\/company\/sanoscience\/","https:\/\/www.youtube.com\/channel\/UCDZ_8TcjMWUG2ZcgKKgfpwQ","https:\/\/bsky.app\/profile\/sanoscience.bsky.social"]}]}},"acf":[],"gutenberg_blocks":[{"blockName":"custom-styles","attrs":{"styles":""}},{"blockName":"core\/heading","attrs":{"epAnimationGeneratedClass":"edplus_anim-2dQgNC","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 class=\"wp-block-heading eplus-wrapper\">Abstract<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\">Abstract<\/h2>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Sh7955","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">In personalised health, small datasets with missing data are quite common. Current Machine Learning methods are unable to process such datasets in a meaningful way due to the huge data volume requirement. To address this problem, we proposed a new Small and iNcomplete Dataset Analyser (SaNDA) to process such datasets in a meaningful way. Due to the characteristics of these datasets and the criticality of the domain, an explainable method was mandatory. Thus, SaNDA prioritised explainability over efficiency. We evaluated our proposal against the current standard of explainable methods: Random Forests. We observed that our proposal outperforms Random Forest when there is more missing data and\/or lower number of entries in the dataset, obtaining less favourable results over typically larger, well-curated datasets. Given the difficulties in obtaining complete, reliable data in the healthcare field, we consider that our proposal could be useful for practitioners.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">In personalised health, small datasets with missing data are quite common. Current Machine Learning methods are unable to process such datasets in a meaningful way due to the huge data volume requirement. To address this problem, we proposed a new Small and iNcomplete Dataset Analyser (SaNDA) to process such datasets in a meaningful way. Due to the characteristics of these datasets and the criticality of the domain, an explainable method was mandatory. Thus, SaNDA prioritised explainability over efficiency. We evaluated our proposal against the current standard of explainable methods: Random Forests. We observed that our proposal outperforms Random Forest when there is more missing data and\/or lower number of entries in the dataset, obtaining less favourable results over typically larger, well-curated datasets. Given the difficulties in obtaining complete, reliable data in the healthcare field, we consider that our proposal could be useful for practitioners.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-AVTePh","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/heading","attrs":{"epAnimationGeneratedClass":"edplus_anim-wYDHMl","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 class=\"wp-block-heading eplus-wrapper\">About the author<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\">About the author<\/h2>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-MfjFvY","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Alfredo Ibias<\/strong>&nbsp;received B.A. degrees in Computer Science and in Mathematics from Complutense University of Madrid, Spain; an M.A. degree in Formal Methods in Computer Science from the same university; and a Ph.D. degree in Computer Science at the same university too. He is currently working at Sano as a Postdoctoral Researcher focused on developing AI methods for healthcare.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Alfredo Ibias<\/strong>&nbsp;received B.A. degrees in Computer Science and in Mathematics from Complutense University of Madrid, Spain; an M.A. degree in Formal Methods in Computer Science from the same university; and a Ph.D. degree in Computer Science at the same university too. He is currently working at Sano as a Postdoctoral Researcher focused on developing AI methods for healthcare.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-7azxuP","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><a href=\"http:\/\/seminar.sano.science\/\"><\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><a href=\"http:\/\/seminar.sano.science\/\"><\/a><\/p>\n"]}],"meta_data":{"event_day":"2023-03-13","event_time":"2:00-3:30 PM (CET)","event_guest":"Alfredo Ibias, Personal Health Data Science Team, Sano Centre for Computational Medicine, Krakow, PL","has_medias":true,"medias":[{"icon":{"ID":1144,"id":1144,"title":"clock","filename":"clock.svg","filesize":1479,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","link":"https:\/\/sano.science\/seminars\/79-digital-behaviour-change-interventions-dbci-from-design-to-implementation\/clock\/","alt":"clock Sano Seminar","author":"7","description":"","caption":"Sano Seminar clock","name":"clock","status":"inherit","uploaded_to":13471,"date":"2023-06-01 13:24:42","modified":"2024-10-09 16:41:04","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":56,"height":57,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","thumbnail-width":147,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","medium-width":294,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","medium_large-width":768,"medium_large-height":783,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","large-width":1004,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","1536x1536-width":56,"1536x1536-height":57,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","2048x2048-width":56,"2048x2048-height":57}},"title":"13rd March 2023, 2:00-3:30 PM (CET)","link":""},{"icon":{"ID":1146,"id":1146,"title":"camera","filename":"camera.svg","filesize":1129,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","link":"https:\/\/sano.science\/seminars\/79-digital-behaviour-change-interventions-dbci-from-design-to-implementation\/camera\/","alt":"camera Sano Seminar","author":"7","description":"","caption":"Sano Seminar camera","name":"camera","status":"inherit","uploaded_to":13471,"date":"2023-06-01 13:25:24","modified":"2024-10-09 16:42:29","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":60,"height":38,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","thumbnail-width":150,"thumbnail-height":95,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","medium-width":300,"medium-height":190,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","medium_large-width":768,"medium_large-height":486,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","large-width":1024,"large-height":648,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","1536x1536-width":60,"1536x1536-height":38,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","2048x2048-width":60,"2048x2048-height":38}},"title":"Join via ZOOM on","link":{"title":"seminar.sano.science","url":"http:\/\/seminar.sano.science","target":"_blank"}}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/13373","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/seminars"}],"version-history":[{"count":5,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/13373\/revisions"}],"predecessor-version":[{"id":13626,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/13373\/revisions\/13626"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/13374"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=13373"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}