Prediction of risk factors and electrocardiographic changes in chronic kidney disease patients
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Sanjaya Kumar Panigrahi
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Madhuchhanda Pattnaik
Abstract
Objectives
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.
Methods
Biosensors capture key parameters, including SpO2 (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.
Results
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).
Conclusions
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).
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Research ethics: The paper has been submitted with full responsibility, following due ethical procedure, and there is no duplicate publication, fraud, plagiarism. None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper. This article does not contain any studies with human participants or animals performed by any of the authors.
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Informed consent: Not applicable.
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Author contributions: All authors contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: Not applicable.
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Conflict of interest: The authors declare that they have no conflict of Interest.
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Research funding: The authors received no funding from an external source.
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Data availability: Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
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