A Population-Based Study on the Prevalence and Risk Factors of Chronic Kidney Disease in Adult Population of Shiraz, Southern Iran

Bakhshayeshkaram, Marzieh; Roozbeh, Jamshid; Heydari, Sayed Taghi; Honarvar, Behnam; Dabbaghmanesh, Mohammad Hossein; Ghoreyshi, Maryam; Lankarani, Kamran Bagheri

doi:10.31661/gmj.v0i0.935

Cited by 13 publications

(4 citation statements)

References 33 publications

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“…It is also found that age, socioeconomic status, gender and geographic region also influences the distribution of CKD patients. The end stage of CKD is expensive [5] and leads to ailment [6,7].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Estimating the mortality rate using statistical variance and reduced set of clinical and non-clinical attributes for diagnosing chronic kidney disease

Meena,

Vadivel,

Sumathy

et al. 2023

Soft Comput

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It is found that, Chronic Kidney Disease (CKD) is prevalence worldwide. Quality of Life (QoL) in terms of health became an essential measure for patients with CKD. This paper uses the real-time dataset of CKD patients collected from reputed medical dialysis unit in Chennai, India. We measure the inter and intra class variations between the clinical and non-clinical attributes. Principal Component Analysis (PCA) is applied on twelve clinical (biomarkers) and eight non-clinical (comorbiditity) attributes to find salient among them. ANOVA is applied only on reduced attributes to calculate the correlation between the target variables such as mortality, age and gender. The characteristics of the attributes and its discriminating nature is evaluated using various well known classifiers such as Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Neive Bayes , Decision Tree , Random Forest and Artificial Neural Network. The performance of the classifiers are evaluated using parameters such as confusion matrix, accuracy, F-measure, precision and Recall. It is found that, the covariance of the attributes linearly separates the output space of target variables that are considered and the performance is encouraged.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Estimating the mortality rate using statistical variance and reduced set of clinical and non-clinical attributes for diagnosing chronic kidney disease

Meena,

Vadivel,

Sumathy

et al. 2023

Soft Comput

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show abstract

“…According to one study, the prevalence of CKD varies by age group, gender, socioeconomic status, and geographic region [7]. Chronic kidney disease (CKD) patients are more prone to developing endstage renal disease (ESRD), which demands expensive treatment methods like dialysis and kidney transplantation [11], and this financial load leads to long-term medical and psychological difficulties [12,13]. Furthermore, on a global scale, CKD is caused by unmanaged diabetes and hypertension, and the prevalence of CKD is now impacted by these two risk factors.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis for Prediction of Kidney Disease Using Intelligent Machine Learning Methods

Ifraz

Rashid

Tazin

et al. 2021

Computational and Mathematical Methods in Medicine

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Chronic kidney disease (CKD) is a major burden on the healthcare system because of its increasing prevalence, high risk of progression to end-stage renal disease, and poor morbidity and mortality prognosis. It is rapidly becoming a global health crisis. Unhealthy dietary habits and insufficient water consumption are significant contributors to this disease. Without kidneys, a person can only live for 18 days on average, requiring kidney transplantation and dialysis. It is critical to have reliable techniques at predicting CKD in its early stages. Machine learning (ML) techniques are excellent in predicting CKD. The current study offers a methodology for predicting CKD status using clinical data, which incorporates data preprocessing, a technique for managing missing values, data aggregation, and feature extraction. A number of physiological variables, as well as ML techniques such as logistic regression (LR), decision tree (DT) classification, and K -nearest neighbor (KNN), were used in this work to train three distinct models for reliable prediction. The LR classification method was found to be the most accurate in this role, with an accuracy of about 97 percent in this study. The dataset that was used in the creation of the technique was the CKD dataset, which was made available to the public. Compared to prior research, the accuracy rate of the models employed in this study is considerably greater, implying that they are more trustworthy than the models used in previous studies as well. A large number of model comparisons have shown their resilience, and the scheme may be inferred from the study’s results.

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“…Similar to the global prevalence rate, the prevalence of CKD is significantly increasing in Iran. CKD, in addition to imposing high costs on the health system, causes long‐term physical and psychological complications . The National Health and Nutrition Examination Survey (NHANES) data have shown that the incidence of CKD stages 1–4 increased significantly over the period of 1999–2004 as compared to the period of 1988–1994 (13.1% vs. 10%) , and this high prevalence is partly attributed to population aging as well as the increased incidence of hypertension and diabetes .…”

mentioning

confidence: 99%

Prevalence of Chronic Kidney Disease in Iranian General Population: A Meta‐Analysis and Systematic Review

et al. 2018

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The aim of this study was to determine the prevalence of CKD in the Iranian general population. In this systematic review and meta-analysis study, international (PubMed, Web of Science, Scopus, and Google scholar) and national databases (SID, Magiran) were searched until December 30, 2017. The quality of the studies was evaluated using the Hoy instrument. Out of 1170 initial studies, 9 studies with a total of 70 605 participants were included in the final stage of this study. Based on the results of the random effect method (95% CI), the overall prevalence of chronic kidney disease in 70 605 people was 15.14% (I = 99.77%). The prevalence of CKD in female patients (18.80%) was 1.7 times higher than in male patients (10.83). The results of the present study indicate a higher than the global average prevalence of CKD in Iran and the need for additional attention of policy makers on better planning for control and implementation of screening programs in high-risk populations, such as patients with hypertension and diabetes.

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A Population-Based Study on the Prevalence and Risk Factors of Chronic Kidney Disease in Adult Population of Shiraz, Southern Iran

Cited by 13 publications

References 33 publications

RETRACTED ARTICLE: Estimating the mortality rate using statistical variance and reduced set of clinical and non-clinical attributes for diagnosing chronic kidney disease

RETRACTED ARTICLE: Estimating the mortality rate using statistical variance and reduced set of clinical and non-clinical attributes for diagnosing chronic kidney disease

Comparative Analysis for Prediction of Kidney Disease Using Intelligent Machine Learning Methods

Prevalence of Chronic Kidney Disease in Iranian General Population: A Meta‐Analysis and Systematic Review

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