{"id":1825,"date":"2026-02-22T22:36:43","date_gmt":"2026-02-22T22:36:43","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1825"},"modified":"2026-02-25T15:28:48","modified_gmt":"2026-02-25T15:28:48","slug":"vol23-no2-art2","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/vol23-no2-art2\/","title":{"rendered":"Prediction of Hospitalization Time and Survivability of Patients with Congestive Heart Failure"},"content":{"rendered":"<p>Jo\u00e3o Carlos Pereira Alves <a href=\"https:\/\/orcid.org\/0009-0009-5315-2933\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Ricardo Menezes Salgado <a href=\"https:\/\/orcid.org\/0000-0002-0989-6259\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Iago Augusto Carvalho <a href=\"https:\/\/orcid.org\/0000-0001-9404-1329\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a> &amp; Eric Batista Ferreira <a href=\"https:\/\/orcid.org\/0000-0003-3361-0908\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Abstract:<\/strong> Congestive heart failure (CHF) is a serious medical condition associated with high mortality rates. To improve prognosis and treatment, exploring new strategies is essential. This study investigates the use of electronic medical records to train machine learning models in predicting survival and hospitalization time for patients with CHF. Using data from 299 patients collected in Faisalabad, Pakistan, a suite of algorithms was evaluated, including MLP, logistic regression, Random Forests, decision tree, k-Nearest Neighbors, Naive Bayes, and Gradient Boosting. The methodology employed the SMOTE technique for class balancing and a rigorous 10-fold stratified cross-validation for performance evaluation. The Random Forest model emerged as the top performer, achieving a mean accuracy of 0.80 (\u00b10.05) and an F1-Score of 0.80 (\u00b10.05) in predicting patient survival. Statistical significance tests confirmed that the superiority of the Random Forest over the worst-performing model (MLP) is statistically significant (p &lt; 0.05). For the prediction of hospitalization time, an error rate of 26% was observed. These findings underscore the statistically validated potential of machine learning models in predicting clinical outcomes in patients with CHF, representing an innovative approach to improve diagnostic efficiency and reduce the impact of CHF on public health.<\/p>\n<p><strong>Keywords:<\/strong> Congestive Heart Failure, Machine Learning, Clinical Prediction, Hospitalization Time, Survivability.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol23-no2-art2\">10.21528\/lnlm-vol23-no2-art2<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2025\/12\/vol23-no2-art2.pdf\">vol23-no2-art2.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2025\/12\/vol23-no2-art2.bib\">vol23-no2-art2.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Jo\u00e3o Carlos Pereira Alves , Ricardo Menezes Salgado , Iago Augusto Carvalho &amp; Eric Batista Ferreira Abstract: Congestive heart failure (CHF) is a serious medical condition associated with high mortality rates. To improve prognosis and treatment, exploring new strategies is <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/vol23-no2-art2\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1811,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1825","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Prediction of Hospitalization Time and Survivability of Patients with Congestive Heart Failure - Learning and NonLinear Models<\/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:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/vol23-no2-art2\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Prediction of Hospitalization Time and Survivability of Patients with Congestive Heart Failure - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Jo\u00e3o Carlos Pereira Alves , Ricardo Menezes Salgado , Iago Augusto Carvalho &amp; Eric Batista Ferreira Abstract: Congestive heart failure (CHF) is a serious medical condition associated with high mortality rates. To improve prognosis and treatment, exploring new strategies is Read More ...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/vol23-no2-art2\/\" \/>\n<meta property=\"og:site_name\" content=\"Learning and NonLinear Models\" \/>\n<meta property=\"article:modified_time\" content=\"2026-02-25T15:28:48+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/vol23-no2-art2\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/vol23-no2-art2\/\",\"name\":\"Prediction of Hospitalization Time and Survivability of Patients with Congestive Heart Failure - Learning and NonLinear Models\",\"isPartOf\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/vol23-no2-art2\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/vol23-no2-art2\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\",\"datePublished\":\"2026-02-22T22:36:43+00:00\",\"dateModified\":\"2026-02-25T15:28:48+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/vol23-no2-art2\/#breadcrumb\"},\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/vol23-no2-art2\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/vol23-no2-art2\/#primaryimage\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/vol23-no2-art2\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Browse issues\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Learning &#038; Nonlinear Models &#8211; L&#038;NLM &#8211; Volume 23 &#8211; N\u00famero 2\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Prediction of Hospitalization Time and Survivability of Patients with Congestive Heart Failure\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"name\":\"Learning and NonLinear Models\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"pt-BR\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\",\"name\":\"Learning and NonLinear Models\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"width\":398,\"height\":94,\"caption\":\"Learning and NonLinear Models\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Prediction of Hospitalization Time and Survivability of Patients with Congestive Heart Failure - Learning and NonLinear Models","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol23-no2\/vol23-no2-art2\/","og_locale":"pt_BR","og_type":"article","og_title":"Prediction of Hospitalization Time and Survivability of Patients with Congestive Heart Failure - Learning and NonLinear Models","og_description":"Jo\u00e3o Carlos Pereira Alves , Ricardo Menezes Salgado , Iago Augusto Carvalho &amp; Eric Batista Ferreira Abstract: Congestive heart failure (CHF) is a serious medical condition associated with high mortality rates. 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