{"id":12668,"date":"2023-07-13T13:14:30","date_gmt":"2023-07-13T11:14:30","guid":{"rendered":"https:\/\/new.sano.science\/?post_type=research&#038;p=12668"},"modified":"2024-01-09T18:27:28","modified_gmt":"2024-01-09T17:27:28","slug":"effect-of-feature-discretization-on-classification-performance-of-explainable-scoring-based-machine-learning-model-2","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/effect-of-feature-discretization-on-classification-performance-of-explainable-scoring-based-machine-learning-model-2\/","title":{"rendered":"Effect of feature discretization on classification performance of explainable scoring-based machine learning model\u00a0"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\"><strong>Pajor A<\/strong>, Sniezynski B, Zolnierek J, <strong>Sitek A<\/strong><\/h2>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">We improve the utility of the Risk-calibrated Supersparse Linear Integer Model (RiskSLIM). It is a scoring system that is an interpretable machine learning classification model optimized for performance. Scoring systems are commonly used in healthcare and justice. We implement feature discretization (FD) in the hyperparameter optimization process to improve classification performance and refer to the new approach as FD-RiskSLIM. We test the approach using two medical applications. We compare the results of FD-RiskSLIM, RiskSLIM, and other machine learning (ML) models. We demonstrate that scoring models based on RiskSLIM, in addition to being interpretable, perform at least on par with the state-of-the-art ML models such as Gradient Boosting in terms of classification metrics. We show the superiority of FD-RiskSLIM over RiskSLIM.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-08757-8_9\" target=\"_blank\" rel= \"noopener noreferrer nofollow\" class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tREAD HERE \n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n","protected":false},"excerpt":{"rendered":"<p>In: 22nd International Conference on Computational Science, 2022.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[17],"class_list":["post-12668","research","type-research","status-publish","hentry","research_type-publications","research_team-health-informatics-group-higs"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Effect of feature discretization on classification performance of explainable scoring-based machine learning model\u00a0 - 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\/research\/effect-of-feature-discretization-on-classification-performance-of-explainable-scoring-based-machine-learning-model-2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Effect of feature discretization on classification performance of explainable scoring-based machine learning model\u00a0\" \/>\n<meta property=\"og:description\" content=\"In: 22nd International Conference on Computational Science, 2022.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/effect-of-feature-discretization-on-classification-performance-of-explainable-scoring-based-machine-learning-model-2\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2024-01-09T17:27:28+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/effect-of-feature-discretization-on-classification-performance-of-explainable-scoring-based-machine-learning-model-2\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/effect-of-feature-discretization-on-classification-performance-of-explainable-scoring-based-machine-learning-model-2\\\/\",\"name\":\"Effect of feature discretization on classification performance of explainable scoring-based machine learning model\u00a0 - 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It is a scoring system that is an interpretable machine learning classification model optimized for performance. Scoring systems are commonly used in healthcare and justice. We implement feature discretization (FD) in the hyperparameter optimization process to improve classification performance and refer to the new approach as FD-RiskSLIM. We test the approach using two medical applications. We compare the results of FD-RiskSLIM, RiskSLIM, and other machine learning (ML) models. We demonstrate that scoring models based on RiskSLIM, in addition to being interpretable, perform at least on par with the state-of-the-art ML models such as Gradient Boosting in terms of classification metrics. We show the superiority of FD-RiskSLIM over RiskSLIM.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">We improve the utility of the Risk-calibrated Supersparse Linear Integer Model (RiskSLIM). It is a scoring system that is an interpretable machine learning classification model optimized for performance. Scoring systems are commonly used in healthcare and justice. We implement feature discretization (FD) in the hyperparameter optimization process to improve classification performance and refer to the new approach as FD-RiskSLIM. We test the approach using two medical applications. We compare the results of FD-RiskSLIM, RiskSLIM, and other machine learning (ML) models. We demonstrate that scoring models based on RiskSLIM, in addition to being interpretable, perform at least on par with the state-of-the-art ML models such as Gradient Boosting in terms of classification metrics. We show the superiority of FD-RiskSLIM over RiskSLIM.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-fdtbdg","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":"acf\/button","attrs":{"title":"READ HERE ","button_type":"link","url":"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-08757-8_9","button_style":"primary","target":"_blank","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/12668","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":8,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/12668\/revisions"}],"predecessor-version":[{"id":14797,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/12668\/revisions\/14797"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=12668"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=12668"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=12668"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}