Chronic Kidney Disease (CKD) is a menace that is affecting 10 percent of the world population and 15 percent of the South African population. The early and cheap diagnosis of this disease with accuracy and reliability will save 20,000 lives in South Africa per year. Scientists are developing smart solutions with Artificial Intelligence (AI). In this paper, several typical and recent AI algorithms are studied in the context of CKD and the extreme gradient boosting (XGBoost) is chosen as our base model for its high performance. Then, the model is optimized and the optimal full model trained on all the features achieves a testing accuracy, sensitivity, and specificity of 1.000, 1.000, and 1.000, respectively. Note that, to cover the widest range of people, the time and monetary costs of CKD diagnosis have to be minimized with fewest patient tests. Thus, the reduced model using fewer features is desirable while it should still maintain high performance. To this end, the set-theory based rule is presented which combines a few feature selection methods with their collective strengths. The reduced model using about a half of the original full features performs better than the models based on individual feature selection methods and achieves accuracy, sensitivity and specificity, of 1.000, 1.000, and 1.000, respectively.

XGBoost Model for Chronic Kidney Disease Diagnosis

Chronic kidney disease (CKD) is among the top 20 causes of death worldwide and affects approximately 10% of the world adult population. CKD is a disorder that disrupts normal kidney function. Due to the increasing number of people with CKD, effective prediction measures for the early diagnosis of CKD are required. The novelty of this study lies in developing the diagnosis system to detect chronic kidney diseases. This study assists experts in exploring preventive measures for CKD through early diagnosis using machine learning techniques. This study focused on evaluating a dataset collected from 400 patients containing 24 features. The mean and mode statistical analysis methods were used to replace the missing numerical and the nominal values. To choose the most important features, Recursive Feature Elimination (RFE) was applied. Four classification algorithms applied in this study were support vector machine (SVM), k-nearest neighbors (KNN), decision tree, and random forest. All the classification algorithms achieved promising performance. The random forest algorithm outperformed all other applied algorithms, reaching an accuracy, precision, recall, and F1-score of 100% for all measures. CKD is a serious life-threatening disease, with high rates of morbidity and mortality. Therefore, artificial intelligence techniques are of great importance in the early detection of CKD. These techniques are supportive of experts and doctors in early diagnosis to avoid developing kidney failure.

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Diagnosis of Chronic Kidney Disease Using Effective Classification Algorithms and Recursive Feature Elimination Techniques

Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP.

Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data.

A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework

The tree-based machine learning functions on the divide-and-conquer principle and is known to perform well in certain applications. In this paper, we first give a new data partitioning rule using the mean of the data columns to grow the tree till the child nodes are small in size. Then, the local regression is applied to leave nodes to enhance the resolution of the node outputs. Randomization is introduced at tree growth and forest creation. The local prediction accuracies on the leaves are used to select a subset of the test data for actual predictions. The case study on the diagnosis of autistic spectrum disorder shows that the proposed method achieves the prediction accuracy of the ensemble at above 96% with reduced variance, which is much better than those reported in the literature.

/pdf/integrated-learning-via-randomized-forests-and-localized-nubpxzmacr.pdf

Integrated Learning via Randomized Forests and Localized Regression With Application to Medical Diagnosis

Chronic kidney disease is a very prevalent ailment in the world; National Kidney Foundation of South Africa estimated that about 15% of the population in South Africa experience kidney disease and about 20,000 yearly reported cases and several thousands die untimely due to this disease. Application of Artifical Intelligence (AI) techniques to our day-to-day lives is bring positive changes, from banking to health carem military, gaming, welfare and so on. Scholars have worked extensively on Chronic Kidney Diseases (CKD) and most of their works are on pure statistical models thereby creating a lot of gaps for Machine Learning (ML) based model to explore. In this paper, we will review existing techniques, and propose a better technique based on Extreme Gradient Boosting (XGBoost) model with a combination of three feature selection technique for a fast and accurate diagnosis of CKD with relevant symptoms. The CKD model developed in this paper has an accuracy of 0.976, which is better than the baseline models currently existing. Also, the sensitivity and specificity of the CKD model for 36 patients is 1.0 and 0.917 respectively. False diagnosis of CKD patients using this model is reduced greatly. The proposed model will reduce the cost of diagnosing CKD and it can be easily embedded in a CDSS.

Enhanced XGBoost-Based Automatic Diagnosis System for Chronic Kidney Disease

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XGBoost Model for Chronic Kidney Disease Diagnosis

Integrated Learning via Randomized Forests and Localized Regression With Application to Medical Diagnosis

Enhanced XGBoost-Based Automatic Diagnosis System for Chronic Kidney Disease