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Chronic kidney disease diagnosis using Fuzzy Knowledge Graph Pairs : based inference in the extreme case

Chuan Pham Minh, Long Cu Kim, Lan Luong Thi Hong, Tuan Tran Manh, Hai Pham Van, Tan Nguyen Hong, Son Le Hoang

Annals of Computer Science and Information Systems

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2022

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Vol. 33

83--88

EN

Chronic kidney disease is one of the diseases with high morbidity and mortality, commonly occurring in the general adult population, especially in people with diabetes and hypertension. Scientists have researched and developed intelligent medical systems to diagnose chronic kidney disease. Nevertheless, healthcare services remain low in resource-limited areas, and general practitioners are very short of clinical experience. Identifying chronic kidney disease in clinical practice remains challenging, especially for the general practitioner. This study proposes a model to develop a model for improving the efficiency of differential diagnosis. This paper presents a model consisting of a fuzzy knowledge graph pairs-based inference mechanism by accumulating the new rules to enrich the fuzzy rule base. A real-world dataset is gathered in Dien Bien hospital to evaluate the performance of our proposed model.

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Data mining approach in diagnosis and treatment of chronic kidney disease

Turiac Andreea S., Zdrodowska Małgorzata

Acta Mechanica et Automatica

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2022

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Vol. 16, no. 3

180--188

EN

Chronic kidney disease is a general definition of kidney dysfunction that lasts more than 3 months. When chronic kidney disease is advanced, the kidneys are no longer able to cleanse the blood of toxins and harmful waste products and can no longer support the proper function of other organs. The disease can begin suddenly or develop latently over a long period of time without the presence of characteristic symptoms. The most common causes are other chronic diseases – diabetes and hypertension. Therefore, it is very important to diagnose the disease in early stages and opt for a suitable treatment - medication, diet and exercises to reduce its side effects. The purpose of this paper is to analyse and select those patient characteristics that may influence the prevalence of chronic kidney disease, as well as to extract classification rules and action rules that can be useful to medical professionals to efficiently and accurately diagnose patients with kidney chronic disease. The first step of the study was feature selection and evaluation of its effect on classification results. The study was repeated for four models – containing all available patient data, containing features identified by doctors as major factors in chronic kidney disease, and models containing features selected using Correlation Based Feature Selection and Chi-Square Test. Sequential Minimal Optimization and Multilayer Perceptron had the best performance for all four cases, with an average accuracy of 98.31% for SMO and 98.06% for Multilayer Perceptron, results that were confirmed by taking into consideration the F1-Score, for both algorithms was above 0.98. For all these models the classification rules are extracted. The final step was action rule extraction. The paper shows that appropriate data analysis allows for building models that can support doctors in diagnosing a disease and support their deci-sions on treatment. Action rules can be important guidelines for the doctors. They can reassure the doctor in his diagnosis or indicate new, previously unseen ways to cure the patient.

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Deep convolutional neural network for chronic kidney disease prediction using ultrasound imaging

Patil Smitha, Choudhary Savita

Bio-Algorithms and Med-Systems

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2021

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Vol. 17, no. 2

137--163

EN

Objectives: Chronic kidney disease (CKD) is a common disease and it is related to a higher risk of cardiovascular disease and end-stage renal disease that can be prevented by the earlier recognition and diagnosis of individuals at risk. Even though risk factors for CKD have been recognized, the effectiveness of CKD risk classification via prediction models remains uncertain. This paper intends to introduce a new predictive model for CKD using US image. Methods: The proposed model includes three main phases “(1) preprocessing, (2) feature extraction, (3) and classification.” In the first phase, the input image is subjected to preprocessing, which deploys image inpainting and median filtering processes. After preprocessing, feature extraction takes place under four cases; (a) texture analysis to detect the characteristics of texture, (b) proposed highlevel feature enabled local binary pattern (LBP) extraction, (c) area based feature extraction, and (d) mean intensity based feature extraction. These extracted features are then subjected for classification, where “optimized deep convolutional neural network (DCNN)” is used. In order to make the prediction more accurate, the weight and the activation function of DCNN are optimally chosen by a new hybrid model termed as diversity maintained hybrid whale moth flame optimization (DM-HWM) model. Results: The accuracy of adopted model at 40th training percentage was 44.72, 11.02, 5.59, 3.92, 3.92, 3.57, 2.59, 1.71, 1.68, and 0.42% superior to traditional artificial neural networks (ANN), support vector machine (SVM), NB, J48, NBtree, LR, composite hypercube on iterated random projection (CHIRP), CNN, moth flame optimization (MFO), and whale optimization algorithm (WOA) models. Conclusions: Finally, the superiority of the adopted scheme is validated over other conventional models in terms of various measures.

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Early stage of chronic kidney disease by using statistical evaluation of the previous measurement results

Akben S. B.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 4

626--631

EN

Chronic kidney disease (CKD) that causes the progressive losses in kidney functions is one of the major public health problems. Expert medical doctors can diagnose the CKD through symptoms, blood and urine tests in its early stages. However, the diagnosis of CKD might be difficult for expert medical doctors in case of the questionable measurement result. Therefore a new mathematical method that would be helpful to the expert medical doctors is required. It can be said that there is no studies related with automatic diagnosis of CKD in the literature. This study aims to remedy this shortcoming in the literature. In this study, for each of test and symptom values, averages of measurement results were calculated separately for healthy subjects and patients. Then the measured values were marked as ‘‘0’’ or ‘‘1’’ (healthy or patient) according to closeness to average values. Finally, the classification was performed by averaging the values marked for each subject. The success rate of the proposed method is between 96% and 98% according to the age ranges. In conclusion section of the study, how to implement the proposed method in real life is offered.

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Simultaneous Determination of Phenol and p-Cresol in Human Urine by an HPLC Method

Tekkeli S. E., Kiziltas M. V., Dincel D., Erkoc R., Topcu G.

Acta Chromatographica

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2016

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Vol. 28, no. 2

255--262

EN

Introduction: In patients with chronic kidney disease (CKD), uremic toxins accumulate in blood and cannot be excreted with urine. Accumulation of these toxins has negative effects on many body functions. Because of the importance of these toxins, we developed and validated a simple, sensitive, accurate, and precise method for the determination of two main uremic toxins: phenol and p-cresol in human urine. Materials and methods: Separation of these analytes in urine samples was achieved by reverse-phase high-performance liquid chromatography (RP-HPLC) with a C18 column at 35 °C using the mobile phase of methanol–water (65:35) at a flow rate of 1.4 mL min−1. Fluorimetric detection was used at 284 nm for excitation and 310 nm for emission. Results: The method is linear over the range of 1.5–35 ng mL−1 and 1–45 ng mL−1 for phenol and p-cresol, respectively. The method was applied to urine samples from 10 healthy subjects and 10 chronic kidney disease patients. Conclusions: This assay appears to be useful in routine analysis of clinical samples for simultaneous determination of phenol and p-cresol levels in urine.