An Analysis on Chronic Kidney Disease Prediction System: Cleaning, Preprocessing, and Effective Classification of Data

2018 ◽  
pp. 473-480
Author(s):  
Ankit ◽  
Bhagyashree Besra ◽  
Banshidhar Majhi
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Disease Prediction ◽  
Prediction System

scholarly journals Chronic Kidney Disease Prediction based on Blood Potassium Levels using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 4194-4199
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Medical Records ◽  
Research Study ◽  
Machine Learning Algorithms ◽  
Disease Prediction ◽  
Blood Potassium ◽  
Gain Access

Machine learning is an artificial intelligence(AI) technology that provides the systems with the knowledge and capability to learn and evolve automatically from specifically programmed experiences. This focuses on designing computer programs that are able to gain access and use information on their own. Kidney damage or decreased activity for more than three months is known as chronic kidney disease.This illness occurs when the kidneys can no longer expel extra water or waste from human blood. The goal of this research study is to prepare a predictive modeling for chronic kidney disease data to analyze the different open source python module and output the results predicted by machine learning algorithms and determine the accuracy by comparing different algorithms such as KNN and Logistic Regression which are primarily used for classification of data. This algorithm makes predictions on a dataset collected from the patient's medical records. It gives us the clarity that if someone has chronic kidney disease or not primarily based on a person's blood potassium levels present in their body.

Clinical classification of hyperuricemia in patients with chronic kidney disease

2021 ◽  
Author(s):  
Fengqin Li ◽  
Hui Guo ◽  
Jianan Zou ◽  
Chensheng Fu ◽  
Song Liu ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Clinical Classification

Development and Validation of a Chronic Kidney Disease Prediction Model for Type 2 Diabetes Mellitus in Thailand

2021 ◽  
Vol 24 ◽  
pp. 157-166
Author(s):  
Wilailuck Tuntayothin ◽  
Stephen John Kerr ◽  
Chanchana Boonyakrai ◽  
Suwasin Udomkarnjananun ◽  
Sumitra Chukaew ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Type 2 Diabetes Mellitus ◽  
Kidney Disease ◽  
Prediction Model ◽  
Disease Prediction ◽  
Development And Validation

scholarly journals Classification of Chronic Kidney Disease Biomarkers to Predict Coronary Artery Calcium

2012 ◽  
Vol 36 (1) ◽  
pp. 26-35 ◽  
Author(s):  
Sterling McPherson ◽  
Celestina Barbosa-Leiker ◽  
Robert Short ◽  
Katherine R. Tuttle
Keyword(s):  
Chronic Kidney Disease ◽  
Coronary Artery ◽  
Kidney Disease ◽  
Coronary Artery Calcium ◽  
Disease Biomarkers

Association of age with risk of major depression among patients with chronic kidney disease over midlife: a nationwide cohort study in Taiwan

2018 ◽  
Vol 31 (08) ◽  
pp. 1171-1179 ◽  
Author(s):  
Shih-Feng Chen ◽  
Yu-Huei Chien ◽  
Pau-Chung Chen ◽  
I-Jen Wang
Keyword(s):  
Chronic Kidney Disease ◽  
Cohort Study ◽  
Major Depression ◽  
Kidney Disease ◽  
Comparison Group ◽  
Age Groups ◽  
International Classification Of Diseases ◽  
Classification Of Diseases ◽  
The Impact

ABSTRACTBackground:The impact of age on the development of depression among patients with chronic kidney disease (CKD) at stages before dialysis is not well known. We aimed to explore the incidence of major depression among predialysis CKD patients of successively older ages through midlife.Methods:We conducted a retrospective cohort study using the longitudinal health insurance database 2005 in Taiwan. This study investigated 17,889 predialysis CKD patients who were further categorized into study (i.e. middle and old-aged) groups and comparison group aged 18–44. The International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) was applied for coding diseases.Results:The group aged 75 and over had the lowest (hazard ratio [HR] 0.47; 95% confidence interval [CI] 0.32–0.69) risk of developing major depression, followed by the group aged 65–74 (HR 0.67; 95% CI 0.49–0.92), using the comparison group as reference. The adjusted survival curves showed significant differences in cumulative major depression-free survival between different age groups. We observed that the risk of major depression development decreases with higher age. Females were at a higher risk of major depression than males among predialyasis CKD patients.Conclusions:The incidence of major depression declines with higher age in predialysis CKD patients over midlife. Among all age groups, patients aged 75 and over have the lowest risk of developing major depression. A female preponderance in major depression development is present. We suggest that depression prevention and therapy should be integrated into the standard care for predialysis CKD patients, especially for those young and female.

scholarly journals A Comprehensive Unsupervised Framework for Chronic Kidney Disease Prediction

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Linta Antony ◽  
Sami Azam ◽  
Eva Ignatious ◽  
Ryana Quadir ◽  
Abhijith Reddy Beeravolu ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Disease Prediction

scholarly journals Molecular Stratification of Chronic Kidney Disease

2021 ◽  
Author(s):  
Anna Reznichenko ◽  
Viji Nair ◽  
Sean Eddy ◽  
Mark Tomilo ◽  
Timothy Slidel ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Cell Types ◽  
Biological Pathways ◽  
Self Organizing Maps ◽  
Molecular Stratification ◽  
Indirect Measures ◽  
Genome Wide ◽  
Neural Network Algorithm

Current classification of chronic kidney disease (CKD) into stages based on the indirect measures of kidney functional state, estimated glomerular filtration rate and albuminuria, is agnostic to the heterogeneity of underlying etiologies, histopathology, and molecular processes. We used genome-wide transcriptomics from patients kidney biopsies, directly reflecting kidney biological processes, to stratify patients from three independent CKD cohorts. Unsupervised Self-Organizing Maps (SOM), an artificial neural network algorithm, assembled CKD patients into four novel subgroups, molecular categories, based on the similarity of their kidney transcriptomics profiles. The unbiased, molecular categories were present across CKD stages and histopathological diagnoses, highlighting heterogeneity of conventional clinical subgroups at the molecular level. CKD molecular categories were distinct in terms of biological pathways, transcriptional regulation and associated kidney cell types, indicating that the molecular categorization is founded on biologically meaningful mechanisms. Importantly, our results revealed that not all biological pathways are equally activated in all patients; instead, different pathways could be more dominant in different subgroups and thereby differentially influencing disease progression and outcomes. This first kidney-centric unbiased categorization of CKD paves the way to an integrated clinical, morphological and molecular diagnosis. This is a key step towards enabling precision medicine for this heterogeneous condition with the potential to advance biological understanding, clinical management, and drug development, as well as establish a roadmap for molecular reclassification of CKD and other complex diseases.

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