Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease
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 cond...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/2/371 |
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| author | Ramesh Chandra Poonia Mukesh Kumar Gupta Ibrahim Abunadi Amani Abdulrahman Albraikan Fahd N. Al-Wesabi Manar Ahmed Hamza Tulasi B |
| author_facet | Ramesh Chandra Poonia Mukesh Kumar Gupta Ibrahim Abunadi Amani Abdulrahman Albraikan Fahd N. Al-Wesabi Manar Ahmed Hamza Tulasi B |
| author_sort | Ramesh Chandra Poonia |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T21:48:45Z |
| format | Article |
| id | doaj.art-3bb251655f424da295ba0d659682fe4c |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T21:48:45Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-3bb251655f424da295ba0d659682fe4c2023-11-23T20:10:47ZengMDPI AGHealthcare2227-90322022-02-0110237110.3390/healthcare10020371Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney DiseaseRamesh Chandra Poonia0Mukesh Kumar Gupta1Ibrahim Abunadi2Amani Abdulrahman Albraikan3Fahd N. Al-Wesabi4Manar Ahmed Hamza5Tulasi B6Department of Computer Science, CHRIST (Deemed to be University), Bangalore 560029, IndiaDepartment of Computer Science & Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur 302017, IndiaDepartment of Information Systems, Prince Sultan University, P.O. Box No. 66833 Rafha Street, Riyadh 11586, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha 61421, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi ArabiaDepartment of Computer Science, CHRIST (Deemed to be University), Bangalore 560029, IndiaKidney 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.https://www.mdpi.com/2227-9032/10/2/371usability score artificial intelligencemedical information systemsimage matchingmachine learning algorithmsmorphological operations |
| spellingShingle | Ramesh Chandra Poonia Mukesh Kumar Gupta Ibrahim Abunadi Amani Abdulrahman Albraikan Fahd N. Al-Wesabi Manar Ahmed Hamza Tulasi B Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease Healthcare usability score artificial intelligence medical information systems image matching machine learning algorithms morphological operations |
| title | Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease |
| title_full | Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease |
| title_fullStr | Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease |
| title_full_unstemmed | Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease |
| title_short | Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease |
| title_sort | intelligent diagnostic prediction and classification models for detection of kidney disease |
| topic | usability score artificial intelligence medical information systems image matching machine learning algorithms morphological operations |
| url | https://www.mdpi.com/2227-9032/10/2/371 |
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