Performance Analysis of Chronic Kidney Disease through Machine Learning Approaches

Emon, Minhaz Uddin; Imran, Al; Islam, Rakibul; Keya, Maria Sultana; Zannat, Raihana; Ohidujjaman,

doi:10.1109/icict50816.2021.9358491

Cited by 31 publications

(7 citation statements)

References 10 publications

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“…Emon et al [5] analysed the performances of 8 Machine Learning algorithms: Naive Bayes (NB), Multilayer Perceptron (MLP), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Adaptive Boosting (Adaboost), Decision Tree (DT), Bagging,and Random Forest (RF). They used principle component analysis for feature extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Transparency is crucial for gaining trust in medical domain projects, particularly when black box algorithms are utilized, which can obscure the internal workings of a model. To address this issue and ensure code interpretability, Shapely values can be plotted using various types of visualization techniques to display the contribution of each feature to a particular classification result [5]. By providing this level of detail, users can better understand the reasoning behind a model's decision-making process.…”

Section: Introductionmentioning

confidence: 99%

“…Performance evaluationHere, SHAP is applied on Random Forest model using training dataset. SHAP plots[5] that are used for visualization and interpretation of output of models are plotted:It shows top 20 features. Larger values are indicated with red shades and smaller values are indicated with blue shades.…”

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confidence: 99%

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Prediction of Chronic Kidney Disease with Machine Learning Models and Feature Analysis Using SHAP

Surekha,

Kodepogu,

Kumari

et al. 2023

RIA

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The world is significantly impacted by chronic kidney disease (CKD), both in terms of the health and financial costs. CKD is becoming a bigger issue globally, especially in low-and middle-income nations. According to the Global Burden of Disease Survey, 697.5 million people worldwide suffered from chronic kidney disease (CKD) in 2019.Addressing the burden of CKD requires a comprehensive approach that includes prevention, earlydetection, and effective management of the condition. The main objective of this research work is to utilize Machine Learning methodologies to facilitate the diagnosis of Chronic Kidney Disease (CKD) by leveraging relevant clinical details. To accomplish this, the classification models Logistic Regression, Random Forest, Voting Classifier and Support Vector Machine are employed to distinguish patients with CKD from those without. The evaluation shows that according to the evaluations matrices Voting Classifier with soft voting showed an average classification accuracy of 98%, f1-score of 97.4%, precision of 95%, recall of 100%. Random Forest classifier showed an average classification accuracy of 96%, f1-score of 95%, precision of 90.4%, recall of 100%. Logistic regression classifier showed an average classification accuracy of 94%, f1-score of 92.6%, precision of 86.3%, recall of 100%. Support Vector Machine classifier showed an average classification accuracy of 90%, f1-score of 88.3%, precision of 79.1%, recall of 100% and proves that Voting Classifier performed well which is immediately followed by Random Forest and then Logistic Regression. Furthermore, the SHAP (SHapley Additive exPlanations) model interpretability technique is utilized to analyze the significance of each feature in determining output.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Chronic Kidney Disease with Machine Learning Models and Feature Analysis Using SHAP

Surekha,

Kodepogu,

Kumari

et al. 2023

RIA

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“…AdaBoost with the reduced feature set, which attained a maximum accuracy of 99.8%. Emon et al [47] used various boosting techniques to predict the risk of CKD progression among patients. The authors applied the principal component analysis (PCA) method to get the optimal feature set and attained the highest accuracy rate of 99.0% using random forest (R.F.).…”

Section: Related Workmentioning

confidence: 99%

Chronic kidney disease prediction using boosting techniques based on clinical parameters

Ganie,

Dutta Pramanik,

Mallik

et al. 2023

PLoS ONE

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Chronic kidney disease (CKD) has become a major global health crisis, causing millions of yearly deaths. Predicting the possibility of a person being affected by the disease will allow timely diagnosis and precautionary measures leading to preventive strategies for health. Machine learning techniques have been popularly applied in various disease diagnoses and predictions. Ensemble learning approaches have become useful for predicting many complex diseases. In this paper, we utilise the boosting method, one of the popular ensemble learnings, to achieve a higher prediction accuracy for CKD. Five boosting algorithms are employed: XGBoost, CatBoost, LightGBM, AdaBoost, and gradient boosting. We experimented with the CKD data set from the UCI machine learning repository. Various preprocessing steps are employed to achieve better prediction performance, along with suitable hyperparameter tuning and feature selection. We assessed the degree of importance of each feature in the dataset leading to CKD. The performance of each model was evaluated with accuracy, precision, recall, F1-score, Area under the curve-receiving operator characteristic (AUC-ROC), and runtime. AdaBoost was found to have the overall best performance among the five algorithms, scoring the highest in almost all the performance measures. It attained 100% and 98.47% accuracy for training and testing sets. This model also exhibited better precision, recall, and AUC-ROC curve performance.

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“…Because of this, eight different machine learning classifiers, including Logistic Regression (LG), Naive Bayes (NB), Multilayer Perceptron (MLP), Stochastic Gradient Descent (SGD), Adaptive Boosting (Adaboost), Bagging, Decision Tree (DT), and Random Forest (RF), are used to measure analysis using weka tools. These classifiers are as follows: Logistic Regression (LG), Naive Bayes (NB), Multilayer Perceptr Through the use of principal component analysis, we do feature extraction on all characteristics (PCA) [8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of kidney disease using machine learning algorithms

Ravindra¹,

Haile²,

Haile³

et al. 2023

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The loss of renal function occurs gradually in diabetic kidney disease (DKD), which is associated with a high death rate. India is second only to China in the number of people living with DKD and it is expected that one million new cases arise in India each year. If diagnosed at an early stage, DKD may be effectively treated. DKD is more dangerous since it often has no early warning signs in its infancy. From a healthcare provider's standpoint, it is crucial to take preventative measures by using a machine-first model to foresee the beginning of DKD. The likelihood that a patient may acquire DKD can be estimated using their health records, and there are open source machine learning methods available to do this. The amount of clinical factors and the number of datasets used to train the algorithm both affect the prediction accuracy. A machine learning method and a booster algorithm were used in this work to increase the accuracy of DKD prediction. The strategy utilized in boosting algorithm produced more reliable outcomes than models used without boosting such as random tree, KNN and support vector machine.

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Performance Analysis of Chronic Kidney Disease through Machine Learning Approaches

Cited by 31 publications

References 10 publications

Prediction of Chronic Kidney Disease with Machine Learning Models and Feature Analysis Using SHAP

Prediction of Chronic Kidney Disease with Machine Learning Models and Feature Analysis Using SHAP

Chronic kidney disease prediction using boosting techniques based on clinical parameters

Prediction of kidney disease using machine learning algorithms

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