Performance based Evaluation ofAlgorithmson Chronic Kidney Disease using Hybrid Ensemble Model in Machine Learning

In medical data science, data classification, pattern generation, data analysis and improving classification accuracy are the important issues in the recent scenario. The main objective of this research to enhanced classification accuracyby four combinations of features technique separately with Neural Network classifier approach.The neural network is analyzed for chronic kidney disease with the help of features reduction and relevanttechniques.In experiment, we used neural network as ensemble model with different features techniques as: Pearson Correlation, Chi-Square, Extra Tree and Lasso regularization. In this research paper, we have prepared training model on 300(75%) instances of chronic kidney disease attributes and testing on 100 (25%) instances.We test the dataset on different applied epochs and calculated accuracy with error rate. The summary of this experiment, we used400 instances with 26 attributes of Chronic Kidney Disease and evaluated highest accuracy calculated (99.98%) with less error rate on passing several epochs by Neural Network ensemble with Lasso model.

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Neural network algorithm to determine the optimal range of PTH level for patients with end-stage chronic kidney disease to maintain bone metabolism

Endocrine Abstracts ◽

10.1530/endoabs.70.aep166 ◽

2020 ◽

Author(s):

Natalia Karlovich ◽

Olga Spiridonova ◽

Tatiana Mokhort

Keyword(s):

Neural Network ◽

Chronic Kidney Disease ◽

Kidney Disease ◽

Bone Metabolism ◽

Optimal Range ◽

Network Algorithm ◽

Neural Network Algorithm ◽

End Stage ◽

Pth Level

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MO460ASSOCIATION BETWEEN CARBAMYLATED ALBUMIN, GUT MICROBIOTA AND THEIR DERIVED METABOLITES IN CHRONIC KIDNEY DISEASE

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfab090.0022 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

Mieke Steenbeke ◽

Sophie Valkenburg ◽

Wim Van Biesen ◽

Joris Delanghe ◽

Marijn Speeckaert ◽

...

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Gut Microbiota ◽

Plasma Levels ◽

Pearson Correlation ◽

Bacterial Species ◽

Study Cohort ◽

Gut Dysbiosis ◽

Symmetry Factor ◽

Cardiovascular Morbidity And Mortality

Abstract Background and Aims Chronic kidney disease (CKD) is characterized by gut dysbiosis. We recently demonstrated a decrease of short-chain fatty acid (SCFA) producing bacterial species with the progression of CKD. Besides, levels of protein-bound uremic toxins (PBUTs) and post-translational modifications of protein are increased in CKD, both are risk factors for accelerated cardiovascular morbidity and mortality. The link between the gut-kidney axis and protein carbamylation is unclear. The aim of the study was to explore the relation between carbamylated albumin, estimated by the albumin symmetry factor, and plasma levels of PBUTs, fecal levels of SCFAs (ongoing), and the abundance of related gut microbiota in different stages of CKD (1-5). Method The study cohort includes 103 non-dialyzed CKD patients (stages 1-5). Serum proteins were detected by capillary electrophoresis and UV absorbance at 214 nm with the symmetry factor as a marker of albumin carbamylation [the lower the symmetry factor, the more carbamylated albumin]. The quantification of PBUTs and SCFAs in plasma and fecal samples, respectively, using validated UPLC methods. Results The Pearson correlation coefficient (r) shows a positive correlation between the albumin symmetry factor and the estimated glomerular filtration rate (eGFR) (r=0.3025; p=0.0019). The albumin symmetry factor correlates positively with the abundance of Butyricicoccus spp. (r= 0.3211; p=0.0009), Faecalibacterium prausnitzii (r=0.2765; p=0.0047) and Roseburia spp. (r=0.2527; p=0.0100) and negatively with the PBUTs, p-cresyl sulfate (pCS) (r=-0.2819; p=0.0039), p-cresyl glucuronide (pCG) (r=-0.2819; p=0.0039) and indoxyl sulfate (IxS) (r=-0.2650; p=0.0068). Conclusion The decreased abundance of SCFA producing gut bacteria with the progression of CKD can evoke unfavorable conditions in the gut. This can contribute to increased plasma levels of PBUTs potentially (indirectly) playing a role in albumin carbamylation. It will be further explored whether fecal levels of SCFAs are affected in parallel and could be potential targets to restore gut dysbiosis and uremia.

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Data Science and real world health data improve precision for diagnosis: Acute Kidney Injury, acute-on-chronic and Chronic Kidney Disease (Preprint)

10.2196/preprints.31356 ◽

2021 ◽

Author(s):

Karen Triep ◽

Alexander Benedikt Leichtle ◽

Martin Meister ◽

Georg Martin Fiedler ◽

Olga Endrich

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Data Science ◽

Query Language ◽

Correct Diagnosis ◽

Kidney Injury ◽

Data Driven ◽

World Health ◽

Discharge Data ◽

The Individual

BACKGROUND The criteria for the diagnosis of kidney disease outlined in “The Kidney Disease: Improving Global Outcomes (KDIGO)” are based on a patient’s current, historical and baseline data. The diagnosis of acute (AKI), chronic (CKD) and acute-on-chronic kidney disease requires past measurements of creatinine and back-calculation and the interpretation of several laboratory values over a certain period. Diagnosis may be hindered by unclear definition of the individual creatinine baseline and rough ranges of norm values set without adjustment for age, ethnicity, comorbidities and treatment. Classification of the correct diagnosis and the sufficient staging improves coding, data quality, reimbursement, the choice of therapeutic approach and the patient’s outcome. OBJECTIVE With the help of a complex rule-engine a data-driven approach to assign the diagnoses acute, chronic and acute-on-chronic kidney disease is applied. METHODS Real-time and retrospective data from the hospital’s Clinical Data Warehouse of in- and outpatient cases treated between 2014 – 2019 is used. Delta serum creatinine, baseline values and admission and discharge data are analyzed. A KDIGO based standard query language (SQL) algorithm applies specific diagnosis (ICD) codes to inpatient stays. To measure the effect on diagnosis, Text Mining on discharge documentation is conducted. RESULTS We show that this approach yields an increased number of diagnoses as well as higher precision in documentation and coding (unspecific diagnosis ICD N19* coded in % of N19 generated 17.8 in 2016, 3.3 in 2019). CONCLUSIONS Our data-driven method supports the process and reliability of diagnosis and staging and improves the quality of documentation and data. Measuring patients’ outcome will be the next step of the project.

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