Diagnostic decision support system of chronic kidney disease using support vector machine

Ahmad, Mubarik; Tundjungsari, Vitri; Widianti, Dini; Amalia, Peny; Rachmawati, Ummi Azizah

doi:10.1109/iac.2017.8280576

Cited by 25 publications

(8 citation statements)

References 9 publications

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“…The extraction of features from an obtained picture may also be used to identify renal illness and condition. References [ 7 , 8 ] examined the kidney features by using a computer-aided diagnosis (CAD) system for the identification of kidney diseases at initial disease. References [ 9 – 12 ] predicted the disease which can be done by utilizing machine learning (ML) approaches.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

Murugesan

Ahmed

Bhola

et al. 2022

BioMed Research International

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Kidney failure occurs whenever the kidney stops to operate properly and would be unable to cleanse or refine the bloodstream as it should. Chronic kidney disease (CKD) is a potentially fatal consequence. If this condition is diagnosed early, its progression can be delayed. There are various factors that increase the likelihood of developing kidney failure. As a consequence, in order to detect this potentially fatal condition early on, these risk factors must be checked on a regular basis before the individual’s health deteriorates. Furthermore, it lowers the cost of therapy. The chronic kidney or renal disease will be recognized in this work utilizing fuzzy and adaptive neural fuzzy inference systems. The fundamental purpose of this initiative is to enhance the precision of medical diagnostics used to diagnose illnesses. Nephron functioning, glucose levels, systolic and diastolic blood pressure, maturity level, weight and height, and smoking are all elements to consider while developing a fuzzy and adaptable neural fuzzy inference system. The output variable describes a specific patient’s stage of chronic renal disease based on input factors such as stage 1, stage 2, stage 3, stage 4, and stage 5. The outcome will show the present stage of a patient’s kidney. As a result, these methods can assist specialists in determining the stage of chronic renal disease. MATLAB software is used to create the fuzzy and neural fuzzy inference systems.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

Murugesan

Ahmed

Bhola

et al. 2022

BioMed Research International

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“…Other applications focused on diagnostic systems for Heart Disease prediction for Coronary diseases using machine learning approaches. The machine learning methods used in these applications ranged between using a single machine learning technique such as hidden Naïve Bayes (NB), SVM, optimized ANN and Decision Tree (DT) classifiers [4,5,6,7], to using a collective or hybrid machine learning techniques [8,9,10]. Since the focus in this proposed research is on heart disease diagnosis, more attention will be devoted to its related literature.…”

Section: Related Workmentioning

confidence: 99%

Cardiovascular Disease Diagnosis: A Machine Learning Interpretation Approach

Meshref¹

2019

IJACSA

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Research on heart diseases has always been the center of attention of the world health organization. More than 17.9 million people died from it in 2016, which represent 31% of the overall deaths globally. Machine learning techniques have been used extensively in that area to assist physicians to develop a firm opinion about the conditions of their heart disease patients. Some of the existing machine learning models still suffers from limited predication ability, and the chosen analysis approaches are not suitable. As well, it was noticed that the existing approaches pay more attention to building high accuracy models, while overlooking the ability to interpret and understand the recommendations of these models. In this research, different renowned machine learning techniques: Artificial Neural Networks, Support Vector Machines, Naïve Bayes, Decision Trees and Random Forests have been investigated to help in building, understanding and interpreting different heart disease diagnosing models. The Artificial Neural Networks model showed the best accuracy of 84.25% compared to the other models. In addition, it was found that despite some designed models have higher accuracies than others, it may be safer to choose a lower accuracy model as a final design of this study. This sacrifice was essential to make sure that a more transparent and trusted model is being used in the heart disease diagnosis process. This transparency validation was conducted using a newly suggested metric: the Feature Ranking Cost index. The use of that index showed promising results by making it clear as which machine learning model has a balance between accuracy and transparency. It is expected that following the detailed analyses and the use of this research findings will be useful to the machine learning community as it could be the basis for post-hoc prediction model interpretation of different clinical data sets.

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“…The model used in the classification process will greatly affect the output and the level of accuracy. An accuracy of 98.34% was successfully generated when the Support Vector Machine (SVM) model was used to predict chronic kidney disease [20]. Meanwhile, according to Sean Shensheng Xu [21], Deep Learning Neural Network managed to outperform SVM and ANN model accuracy.…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 Using Recurrent Neural Network Model

Alamsyah

Prasetiyo

Hakim

et al. 2021

SJI

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The COVID-19 case that infected humans was first discovered in China at the end of 2019. Since then, COVID-19 has spread to almost all countries in the world. To overcome this problem, it takes a quick effort to identify humans infected with COVID-19 more quickly. One of the alternative diagnoses for potential COVID-19 disease is Recurrent Neural Network (RNN). In this paper, RNN is implemented using the Elman network and applied to the COVID-19 dataset from Kaggle. The dataset consists of 70% training data and 30% test data. The learning parameters used were the maximum epoch, learning late, and hidden nodes. The research results show the percentage of accuracy is 88.

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Diagnostic decision support system of chronic kidney disease using support vector machine

Cited by 25 publications

References 9 publications

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

Cardiovascular Disease Diagnosis: A Machine Learning Interpretation Approach

Prediction of COVID-19 Using Recurrent Neural Network Model

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