The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods

Çelik, Enes; Atalay, Muhammet; Kondiloglu, Adil

doi:10.18201/ijisae.265967

Cited by 17 publications

(11 citation statements)

References 6 publications

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“…The preprocessing method was used to replace missing value, then for the data separation using the k-fold cross validation method with a value of k = 2,3,4,5,6. Another research was conducted by Celik et al, [9], in this study obtained in the accuracy result of 96.7% for the C4.5 algorithm.…”

Section: Introductionmentioning

confidence: 51%

The Implementation of Z-Score Normalization and Boosting Techniques to Increase Accuracy of C4.5 Algorithm in Diagnosing Chronic Kidney Disease

Prihanditya¹,

Alamsyah

2020

joscex

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In the health sector, data mining can be used as a recommendation to predict a disease from the collection of patient medical record data or health data. One of the techniques can be applied is classification with the C4.5 algorithm. The increasing accuracy can be conducted in data transformation using zscore normalization method. In addition, the implementation of the ensemble method can also improve accuracy of C4.5 algorithm, namely boosting or adaboost. The purpose of this study was determinin the implementation of zscore normalization in the pre-processing and adaboost stages of the C4.5 algorithm and determing the accuracy of the C4.5 algorithm after applying zscore and adaboost normalization in diagnosing chronic kidney disease. In this study, the mining process used k-fold cross validation with the default value k = 10. The implementation of the C4.5 algorithm obtained an accuracy of 96% while the accuracy of the C4.5 algorithm with the zscore normalization method obtained an accuracy of 96.75%. The highest accuracy was obtained from the addition of the boosting method to the C4.5 algorithm and zscore normalization obtained the accuracy of 97.25%. The increasing accuracy was obtained of 1.25% which compared to the accuracy C4.5 algorithm.

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Section: Introductionmentioning

confidence: 51%

The Implementation of Z-Score Normalization and Boosting Techniques to Increase Accuracy of C4.5 Algorithm in Diagnosing Chronic Kidney Disease

Prihanditya¹,

Alamsyah

2020

joscex

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“…Enes et.al. [14] focused on identifying the most important predictors of early kidney disease diagnosis, with the goal of facilitating earlier patient treatment and avoiding consequences. Diagnosis and prognosis were the focus of their research, which used data from 150 healthy individuals and 250 people with chronic kidney disease in order to draw conclusions.…”

Section: Related Workmentioning

confidence: 99%

Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier

Qadir

Faiq

2023

KJAR

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There are several disease kinds in global populations that may be related to human lifestyles, social, genetic, economic, and other factors related to the nature of the country they live in. Most of the recent studies have focused on investigating prevalent diseases that spread in the population in order to minimize mortality risks, choose the best method for treatment, and improve community healthcare. Kidney disease is one of the most widespread health problems in modern society. This study focuses on kidney stones, cysts, and tumors, the three most common types of renal illness, using a dataset of 12,446 CT urogram and whole abdomen images, aiming to move toward an AI-based kidney disease diagnosis system while contributing to the wider field of artificial intelligence research. In this study, a hybrid technique is used by utilizing both pre-train models for feature extraction and classification using machine learning algorithms for the task of kidney disease image diagnosis. The pre-trained model used in this study is the Densenet-201 model. As well as using Random Forest for classification, the Densenet-201-Random-Forest approach has outperformed many of the previous models used in other studies, having an accuracy rate of 99.719 percent.

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“…Diversity-validated to MIMO antenna configurations. Similarly, a broadband, smallfootprint planar Yagi antenna (BSF PY) in-package proposal for 5G wireless communications was presented in [35]. Monopole taper radiators are used in the recommended Yagi antenna design to enhance bandwidth, decrease size, and make feeding easier.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Statistical Review Evaluation of 5G Antenna Design Models from a Pragmatic Perspective under Multi-Domain Application Scenarios

Srinubabu,

Rajasekhar

2023

IJRITCC

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Antenna design for the 5G spectrum requires analysis of contextual frequency bands, design of miniaturization techniques, gain improvement models, polarization techniques, standard radiation pattern designs, metamaterial integration, and substrate selection. Most of these models also vary in terms of qualitative & and quantitative parameters, which include forward gain levels, reverse gain, frequency response, substrate types, antenna shape, feeding levels, etc. Due to such a wide variety in performance, it is ambiguous for researchers to identify the optimum models for their application-specific use cases. This ambiguity results in validating these models on multiple simulation tools, which increases design delays and the cost of deployments. To reduce this ambiguity, a survey of recently proposed antenna design models is discussed in this text. This discussion recommended that polarization optimization and gain maximization are the major impact factors that must be considered while designing antennas. It is also recommended that collocated microstrip slot antennas, fully planar dual-polarized broadband antennas, and real-time deployments of combined slot antenna pairs with wide-band decoupling are very advantageous. Based on this discussion, researchers will be able to identify optimal performance-specific models for different applications. This discussion also compares underlying models in terms of their quantitative parameters, which include forward gain levels, bandwidth, complexity of deployment, scalability, and cost metrics. Upon referring to this comparison, researchers will be able to identify the optimum models for their performance-specific use cases. This review also formulates a novel Antenna Design Rank Metric (ADRM) that combines the evaluated parameters, thereby allowing readers to identify antenna design models that are optimized for multiple parameters and can be used for large-scale 5G communication scenarios.

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The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods

Cited by 17 publications

References 6 publications

The Implementation of Z-Score Normalization and Boosting Techniques to Increase Accuracy of C4.5 Algorithm in Diagnosing Chronic Kidney Disease

The Implementation of Z-Score Normalization and Boosting Techniques to Increase Accuracy of C4.5 Algorithm in Diagnosing Chronic Kidney Disease

Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier

Statistical Review Evaluation of 5G Antenna Design Models from a Pragmatic Perspective under Multi-Domain Application Scenarios

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