An Empirical Evaluation of Machine Learning Techniques for Chronic Kidney Disease Prophecy

Khan, Bilal; Naseem, Rashid; Muhammad, Fazal; Abbas, Ghulam; Kim, Sunghwan

doi:10.1109/access.2020.2981689

Cited by 94 publications

(48 citation statements)

References 39 publications

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“…In Table 6, we compare the proposed method with other recent CKD prediction research works, including an optimized XGBoost method [38], a probabilistic neural network (PNN) [39], and a method using adaptive boosting (AdaBoost) [40]. The other research works include a hybrid classifier of NB and decision tree (NBTree) [41], XGBoost [42], and a 7-7-1 MLP neural network [43]. Table 6.…”

Section: Methods Accuracy (%)mentioning

confidence: 99%

Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis

2020

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In recent times, several machine learning models have been built to aid in the prediction of diverse diseases and to minimize diagnostic errors made by clinicians. However, since most medical datasets seem to be imbalanced, conventional machine learning algorithms tend to underperform when trained with such data, especially in the prediction of the minority class. To address this challenge and proffer a robust model for the prediction of diseases, this paper introduces an approach that comprises of feature learning and classification stages that integrate an enhanced sparse autoencoder (SAE) and Softmax regression, respectively. In the SAE network, sparsity is achieved by penalizing the weights of the network, unlike conventional SAEs that penalize the activations within the hidden layers. For the classification task, the Softmax classifier is further optimized to achieve excellent performance. Hence, the proposed approach has the advantage of effective feature learning and robust classification performance. When employed for the prediction of three diseases, the proposed method obtained test accuracies of 98%, 97%, and 91% for chronic kidney disease, cervical cancer, and heart disease, respectively, which shows superior performance compared to other machine learning algorithms. The proposed approach also achieves comparable performance with other methods available in the recent literature.

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Section: Methods Accuracy (%)mentioning

confidence: 99%

Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis

2020

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“…A number of studies have shown that feature selection improves the generalization capabilities of the classification models [6][7][8][9][10][11][12]. Feature selection is similar to dimensionality reduction whereas the objective of the former is to retain the semantics present in the original dataset while the latter transforms the data in such a manner as the overall dimensions of the data are reduced [13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the domain of medical data mining, several intelligent clinical decision support systems are designed which tend to automate the diagnosis process [6,42]. These decision systems employ machine learning techniques that assist physicians in the diagnosis and treatment of CKD in an efficient manner [6,7,8]. Based on a number of important indicators such as blood pressure, albumin levels, blood and urea tests, potassium, and other comorbidities e.g.…”

Section: Introductionmentioning

confidence: 99%

“…In a number of studies performed on CKD diagnosis, decision tree models have consistently produced results with a comparatively higher performance [7,8,40]. Hence, the main impetus for using tree-based models in an ensemble technique is two-fold.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, tree models are easy to interpret by the domain experts, therefore in domains such as medical diagnosis, it is desirable to assess the validity of the classification model through visual inspection [37,[42][43]. Secondly, treemodels that are based on bagging and boosting techniques tend to produce highly accurate classifiers on small to medium datasets [7,6,8,23]. Hence, tree models are suitable approaches for considering in an ensemble for a CKD dataset, as they can cater to both types of requirements i.e., interpretability and accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cost-sensitive Ensemble Feature Ranking and Automatic Threshold Selection for Chronic Kidney Disease Diagnosis

Ali¹,

Ali²,

Hussain³

et al. 2020

Preprint

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Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. In this research, we argue that unlike general-purpose classification problems, medical applications, such as chronic kidney disease (CKD) diagnosis, require special treatment. In the case of CKD, apart from model performance, other factors such as the cost of data acquisition may also be taken into account to enhance the applicability of the automated diagnosis system. In this research, we have proposed two techniques for cost-sensitive feature ranking. An ensemble of decision tree models is employed in both the techniques for computing the worth of a feature in the CKD dataset. An automatic threshold selection heuristic is also introduced which is based on the intersection of features’ worth and their accumulated cost. A set of experiments are conducted to evaluate the efficacy of the proposed techniques on both tree-based and non-tree based classification models. The proposed approaches are also evaluated against several comparative techniques. Furthermore, it is demonstrated that the proposed techniques select around 1/4th of the original CKD features while reducing the cost by a factor of 7.42 of the original feature set. Based on the extensive experimentation it is concluded that the proposed techniques employing feature-cost interaction heuristic tend to select feature subsets that are both useful and cost-effective.

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Enhanced Prediction of Chronic Kidney Disease Using Feature Selection and Boosted Classifiers

Mienye

Obaido

Aruleba

et al. 2022

Lecture Notes in Networks and Systems

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An Empirical Evaluation of Machine Learning Techniques for Chronic Kidney Disease Prophecy

Cited by 94 publications

References 39 publications

Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis

Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis

Cost-sensitive Ensemble Feature Ranking and Automatic Threshold Selection for Chronic Kidney Disease Diagnosis

Enhanced Prediction of Chronic Kidney Disease Using Feature Selection and Boosted Classifiers

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