ArticleSensors (Basel, Switzerland)2021
Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 27 citations in OpenAlex.
- Substrate-Dependent Performance of ZnTTBPc-PMMA Composite Films on Rigid, Flexible, and Sustainable Materials for Wearable Devices.Polymers · 2025Article
- A novel adaptive weight bi-directional long short-term memory (AWBi-LSTM) classifier model for heart stroke risk level prediction in IoT.PeerJ. Computer science · 2024Article
- Characterizing the key genes of COVID-19 that regulate tumor immune microenvironment and prognosis in hepatocellular carcinoma.Functional & integrative genomics · 2023Article
- Improvement of a prediction model for heart failure survival through explainable artificial intelligence.Frontiers in cardiovascular medicine · 2023Article
- Smart and Future Applications of Internet of Multimedia Things (IoMT) Using Big Data Analytics.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
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Abstract
The incidence of cardiovascular diseases and cardiovascular burden (the number of deaths) are continuously rising worldwide. Heart disease leads to heart failure (HF) in affected patients. Therefore any additional aid to current medical support systems is crucial for the clinician to forecast the survival status for these patients. The collaborative use of machine learning and IoT devices has become very important in today's intelligent healthcare systems. This paper presents a Public Key Infrastructure (PKI) secured IoT enabled framework entitled Cardiac Diagnostic Feature and Demographic Identification (CDF-DI) systems with significant Models that recognize several Cardiac disease features related to HF. To achieve this goal, we used statistical and machine learning techniques to analyze the Cardiac secondary dataset. The Elevated Serum Creatinine (SC) levels and Serum Sodium (SS) could cause renal problems and are well established in HF patients. The Mann Whitney U test found that SC and SS levels affected the survival status of patients (
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