Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease

• Published in: Healthcare (Basel) Vol. 10; no. 2; p. 371
• Main Authors: Poonia, Ramesh Chandra, Gupta, Mukesh Kumar, Abunadi, Ibrahim, Albraikan, Amani Abdulrahman, Al-Wesabi, Fahd N., Hamza, Manar Ahmed, B, Tulasi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 14.02.2022; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Blood pressure; Datasets; Decision trees; Diabetes; Electronic health records; Feature selection; Hypertension; Illnesses; Industrialized nations; Kidney diseases; Kidney transplants; Machine learning; Medical diagnosis; Mortality; Neural networks; Patients; Support vector machines; Urine; medical information systems; machine learning algorithms; image matching; morphological operations; usability score artificial intelligence
• ISSN: 2227-9032; 2227-9032
• DOI: 10.3390/healthcare10020371

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.