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. 2022 Feb 14;10(2):371.
doi: 10.3390/healthcare10020371.

Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease

Ramesh Chandra Poonia  1 , Mukesh Kumar Gupta  2 , Ibrahim Abunadi  3 , Amani Abdulrahman Albraikan  4 , Fahd N Al-Wesabi  5 , Manar Ahmed Hamza  6 , Tulasi B  1
Affiliations

Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease

Ramesh Chandra Poonia et al. Healthcare (Basel). .

Abstract

Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits.

Keywords: image matching; machine learning algorithms; medical information systems; morphological operations; usability score artificial intelligence.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Detection of chronic kidney disease using recursive feature elimination and classification algorithms. CKD: Chronic Kidney Disease; SVM: Support Vector Machine; KNN: K-Nearest Neighbors.
Figure 2
Figure 2
Flow chart of the proposed model. LR: Logistic Regression; NB: Naïve Bayes; SVM: Support Vector Machine; KNN: Nearest Neighbors; ANN: Artificial Neural Network; RFE: Recursive Feature Elimination.
Figure 3
Figure 3
Results of the prediction models with all features. SVM: Support Vector Machine; KNN: K-Nearest Neighbors; ANN: Artificial Neural Network.
Figure 4
Figure 4
Comparison of LR Models with and without RFE feature selection. RFE: Recursive Feature Selection.
Figure 5
Figure 5
Results of the LR prediction model with Chi-Square feature selection.
Figure 6
Figure 6
Results of the models with and without feature selection. LR: Logistic Regression; REF: Recursive Feature Elimination.
See this image and copyright information in PMC

References

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