Classification of Chronic Kidney Disease using Logistic Regression, Feedforward Neural Network and Wide &amp; Deep Learning

Imran, Abdullah Al; Amin, Nur; Johora, Fatema Tuj

doi:10.1109/ciet.2018.8660844

Cited by 38 publications

(12 citation statements)

References 9 publications

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“…Al Imran et al examined the use of machine learning techniques to analyze datasets for chronic renal disease. For statistical analysis such as F1-score, Precision, Recall, and AUC, the authors employed Logistic Regression and Feed forward Neural Network and generated better results than previous algorithms [15].…”

Section: Related Workmentioning

confidence: 99%

Chronic Kidney Disease Prediction Using Machine Learning Techniques

Pal

2022

Biomedical Materials & Devices

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Chronic kidney disease (CKD) is a life-threatening condition that can be difficult to diagnose early because there are no symptoms. The purpose of the proposed study is to develop and validate a predictive model for the prediction of chronic kidney disease. Machine learning algorithms are often used in medicine to predict and classify diseases. Medical records are often skewed. We have used chronic kidney disease dataset from UCI Machine learning repository with 25 features and applied three machine learning classifiers Logistic Regression (LR), Decision Tree (DT), and Support Vector Machine (SVM) for analysis and then used bagging ensemble method to improve the results of the developed model. The clusters of the chronic kidney disease dataset were used to train the machine learning classifiers. Finally, the Kidney Disease Collection is summarized by category and non-linear features. We get the best result in the case of decision tree with accuracy of 95.92%. Finally, after applying the bagging ensemble method we get the highest accuracy of 97.23%.

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Section: Related Workmentioning

confidence: 99%

Chronic Kidney Disease Prediction Using Machine Learning Techniques

Pal

2022

Biomedical Materials & Devices

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“…Jeewantha et al applied the percentage split method on the dataset, demonstrating most classifiers have better accuracy when percentage of training data is higher, with the MLP as the most accurate model (98.66%). The only study identified where cross-validation technique were not applied was performed by Imran et al [11] obtaining a 99% of F1-score, precision, recall and area under the curve ROC (Roc Auc) with a model based on Feedforward neural networks over unseen samples of the test set. In addition, Van Eyck et al [20] achieved in 2016 the best results so far with a 100% in terms of accuracy, precision, sensivity and specificity by using RF.…”

Section: Related Workmentioning

confidence: 99%

“…As Table 1 shows, the results obtained by the different studies are almost perfect in terms of accuracy (values close to 100%). However, it must be noted that all papers reviewed, except one (Imran et al [11]), performed the cross-validation technique to obtain their results. This technique allows using every sample of the dataset to train the model.…”

Section: Related Workmentioning

confidence: 99%

“…that requires processing and analysis for knowledge extraction [10]. Machine learning and data mining had been successfully applied, over the past few decades, to build computer-aided diagnosis (CAD) systems for diagnosing complex health issues with good accuracy and efficiency by recognizing potentially useful, original, and comprehensible patterns in health data [11,12]. Data Mining is particularly useful in medicine when no availability of evidence favoring a certain treatment option is found.…”

Section: Introductionmentioning

confidence: 99%

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Chronic Kidney Disease Early Diagnosis Enhancing by Using Data Mining Classification and Features Selection

Moreno-Sánchez¹

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Chronic Kidney Disease (CKD) is currently a worldwide chronic disease with an increasing incidence, prevalence and high cost to health systems. A delayed recognition and prevention often lead to a premature mortality due to progressive and incurable loss of kidney function. Data mining classifiers employment to discover patterns in CKD indicators would contribute to an early diagnosis that allow patients to prevent such kidney severe damage. Adopting the cross Industry Standard Process of Data Mining (CRISP-DM) methodology, this work develops a classifier model that would support healthcare professionals in early diagnosis of CKD patients. By building a data pipeline that manages the different phases of CRISP-DM, an automated data transformation, modelling and evaluation is applied to the CKD dataset extracted from the UCI ML repository. Moreover, the pipeline along with the Scikit-learn package's GridSearchCV is used to carry out an exhaustive search of the best data mining classifier and the different parameters of the data preparation's sub-stages like data missing and feature selection. Thus, AdaBoost is selected as the best classifier and it outperforms with a 100% in terms of accuracy, precision, sensivity, specificity, f1-score and roc auc, the classification results obtained by the related works reviewed. Moreover, the application of feature selection reduces up to 12 out of 24 features which are employed in the classifier model developed.

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“…On this dataset, Shawan [16] and Abrar [18] employed several data mining methods for patient classification in their PhD theses. Wibawa et al [8] applied a correlation-based feature selection methods and AdaBoost to this dataset, while Al Imran et al [13] employed deep learning techniques to the same end. a number of machine learning methods for patient classification and described the dataset precisely.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Analysis of Health Records of Patients With Chronic Kidney Disease at Risk of Cardiovascular Disease

Chicco¹,

Lovejoy

Oneto³

2021

IEEE Access

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Chronic kidney disease (CKD) describes a long-term decline in kidney function and has many causes. It affects hundreds of millions of people worldwide every year. It can have a strong negative impact on patients, especially when combined with cardiovascular disease (CVD): patients with both conditions have lower survival chances. In this context, computational intelligence applied to electronic health records can provide insights to physicians that can help them make better decisions about prognoses or therapies. In this study we applied machine learning to medical records of patients with CKD and CVD. First, we predicted if patients develop severe CKD, both including and excluding information about the year it occurred or date of the last visit. Our methods achieved top mean Matthews correlation coefficient (MCC) of +0.499 in the former case and a mean MCC of +0.469 in the latter case. Then, we performed a feature ranking analysis to understand which clinical factors are most important: age, eGFR, and creatinine when the temporal component is absent; hypertension, smoking, and diabetes when the year is present. We then compared our results with the current scientific literature, and discussed the different results obtained when the time feature is excluded or included. Our results show that our computational intelligence approach can provide insights about diagnosis and and relative important of different clinical variables that otherwise would be impossible to observe.

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Classification of Chronic Kidney Disease using Logistic Regression, Feedforward Neural Network and Wide & Deep Learning

Cited by 38 publications

References 9 publications

Chronic Kidney Disease Prediction Using Machine Learning Techniques

Chronic Kidney Disease Prediction Using Machine Learning Techniques

Chronic Kidney Disease Early Diagnosis Enhancing by Using Data Mining Classification and Features Selection

A Machine Learning Analysis of Health Records of Patients With Chronic Kidney Disease at Risk of Cardiovascular Disease

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