{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T05:17:31Z","timestamp":1775798251326,"version":"3.50.1"},"reference-count":26,"publisher":"Walter de Gruyter GmbH","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_jbcpp-2024-0068_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Chronic kidney disease (CKD) is a global health issue with significant morbidity and mortality, particularly due to cardiovascular events. Early identification and management of risk factors are crucial to prevent CKD progression and complications. CKD is heterogeneous with diverse etiologies and presentations, generalizing across populations challenging. This study aims to develop accurate predictive models for cardiovascular events in CKD patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_jbcpp-2024-0068_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Biosensors capture key parameters, including SpO\n                      <jats:sub>2<\/jats:sub>\n                      (Oxygen saturation), PR (Pulse rate), Pi (Perfusion index), RRp (Respiration rate), and PVi (Pleth variability index), enabling comprehensive evaluation of physiological dynamics in CKD patients. Stacked Auto-Encoders (SAEs) are applied for diagnostics. Genetic risk score (GRS) and nongenetic risk score (NGRS) models are developed using natural logarithms of odds ratios (OR) of risk factors.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_jbcpp-2024-0068_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The models integrate properties of each factor with weighted contributions to create predictive models for CKD. A novel machine learning technique incorporates automatic machine learning (AutoML).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_jbcpp-2024-0068_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The models integrate properties of each factor with weighted contributions to create predictive models for CKD. A novel machine learning technique incorporates automatic machine learning (AutoML).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/jbcpp-2024-0068","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T18:28:52Z","timestamp":1770056932000},"page":"119-131","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of risk factors and electrocardiographic changes in chronic kidney disease patients"],"prefix":"10.1515","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3567-260X","authenticated-orcid":false,"given":"Sanjaya Kumar","family":"Panigrahi","sequence":"first","affiliation":[{"name":"Department of Physiology , 627109MKCG Medical College and Hospital , Berhampur , Ganjam, 760004 , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madhuchhanda","family":"Pattnaik","sequence":"additional","affiliation":[{"name":"Department of Physiology , Dharanidhar Medical College and Hospital , Keonjhar , Odisha , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aruna","family":"Acharya","sequence":"additional","affiliation":[{"name":"Department of Nephrology , S.C.B. 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