{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T12:49:25Z","timestamp":1765889365974},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685335","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,8,22]]},"abstract":"<jats:p>Heart Failure (HF) is a life-threatening condition. It affects more than 64 million people worldwide. Early diagnosis of HF is extremely crucial. In this study, we propose utilization of machine learning (ML) models to predict severity of HF from primary Electronic Health Records (EHRs). We used a public dataset of 2008 HF patients for the study. Gaussian Naive Bayes, Random Forest and CatBoost methods were used for prediction. The study shows that CatBoost works best for the goal. In addition to that, the largest contributors for tree-based models harmonize well with clinically important parameters, which exhibits the trustworthiness of these models. Hence, we conclude that utilization of ML methods on primary EHRs is a promising step for time-efficient diagnosis of HF patients.<\/jats:p>","DOI":"10.3233\/shti240471","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:30:28Z","timestamp":1724405428000},"source":"Crossref","is-referenced-by-count":1,"title":["Explainable Machine Learning Based Prediction of Severity of Heart Failure Using Primary Electronic Health Records"],"prefix":"10.3233","author":[{"given":"Rajarajeswari","family":"Ganesan","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Eindhoven University of Technology, The Netherlands"}]},{"given":"Simon C.","family":"Habraken","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Eindhoven University of Technology, The Netherlands"}]},{"given":"Frans N.","family":"van de Vosse","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Eindhoven University of Technology, The Netherlands"}]},{"given":"Wouter","family":"Huberts","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Eindhoven University of Technology, The Netherlands"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Digital Health and Informatics Innovations for Sustainable Health Care Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240471","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:30:28Z","timestamp":1724405428000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240471"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240471","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}