Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease
- Title
- Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease
- Creator
- Poonia R.C.; Gupta M.K.; Abunadi I.; Albraikan A.A.; Al-Wesabi F.N.; Hamza M.A.; Tulasi B.
- Description
- 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. 2022 by the authors.
- Source
- Healthcare (Switzerland), Vol-10, No. 2
- Date
- 2022-01-01
- Publisher
- MDPI
- Subject
- Image matching; Machine learning algorithms; Medical information systems; Morphological operations; Usability score artificial intelligence
- Coverage
- Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Gupta M.K., Department of Computer Science & Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur, 302017, India; Abunadi I., Department of Information Systems, Prince Sultan University, P.O. Box No. 66833 Rafha Street, Riyadh, 11586, Saudi Arabia; Albraikan A.A., Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Al-Wesabi F.N., Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha, 61421, Saudi Arabia; Hamza M.A., Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, 16273, Saudi Arabia; Tulasi B., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India
- Rights
- All Open Access; Gold Open Access; Green Open Access
- Relation
- ISSN: 22279032
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Poonia R.C.; Gupta M.K.; Abunadi I.; Albraikan A.A.; Al-Wesabi F.N.; Hamza M.A.; Tulasi B., “Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/15219.