Unlocking Precision Medicine for Prognosis of Chronic Kidney Disease Using Machine Learning
Chronic kidney disease (CKD) is a significant global health challenge that requires timely detection and accurate prognosis for effective treatment and management. The application of machine learning (ML) algorithms for CKD detection and prediction holds promising potential for improving patient out...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/19/3151 |
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| author | Yogita Dubey Pranav Mange Yash Barapatre Bhargav Sable Prachi Palsodkar Roshan Umate |
| author_facet | Yogita Dubey Pranav Mange Yash Barapatre Bhargav Sable Prachi Palsodkar Roshan Umate |
| author_sort | Yogita Dubey |
| collection | DOAJ |
| description | Chronic kidney disease (CKD) is a significant global health challenge that requires timely detection and accurate prognosis for effective treatment and management. The application of machine learning (ML) algorithms for CKD detection and prediction holds promising potential for improving patient outcomes. By incorporating key features which contribute to CKD, these algorithms enhance our ability to identify high-risk individuals and initiate timely interventions. This research highlights the importance of leveraging machine learning techniques to augment existing medical knowledge and improve the identification and management of kidney disease. In this paper, we explore the utilization of diverse ML algorithms, including gradient boost (GB), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), histogram boost (HB), and XGBoost (XGB) to detect and predict chronic kidney disease (CKD). The aim is to improve early detection and prognosis, enhancing patient outcomes and reducing the burden on healthcare systems. We evaluated the performance of the ML algorithms using key metrics like accuracy, precision, recall, and F1 score. Additionally, we conducted feature significance analysis to identify the most influential characteristics in the detection and prediction of kidney disease. The dataset used for training and evaluation contained various clinical and demographic attributes of patients, including serum creatinine level, blood pressure, and age, among others. The proficiency analysis of the ML algorithms revealed consistent predictors across all models, with serum creatinine level, blood pressure, and age emerging as particularly effective in identifying individuals at risk of kidney disease. These findings align with established medical knowledge and emphasize the pivotal role of these attributes in early detection and prognosis. In conclusion, our study demonstrates the effectiveness of diverse machine learning algorithms in detecting and predicting kidney disease. The identification of influential predictors, such as serum creatinine level, blood pressure, and age, underscores their significance in early detection and prognosis. By leveraging machine learning techniques, we can enhance the accuracy and efficiency of kidney disease diagnosis and treatment, ultimately improving patient outcomes and healthcare system effectiveness. |
| first_indexed | 2024-03-10T21:47:07Z |
| format | Article |
| id | doaj.art-dc6421adeafd4d7e8022d2c5bf577994 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T21:47:07Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-dc6421adeafd4d7e8022d2c5bf5779942023-11-19T14:15:30ZengMDPI AGDiagnostics2075-44182023-10-011319315110.3390/diagnostics13193151Unlocking Precision Medicine for Prognosis of Chronic Kidney Disease Using Machine LearningYogita Dubey0Pranav Mange1Yash Barapatre2Bhargav Sable3Prachi Palsodkar4Roshan Umate5Department of Electronics and Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, IndiaDepartment of Electronics and Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, IndiaDepartment of Electronics and Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, IndiaDepartment of Electronics and Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, IndiaDepartment of Electronics Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, IndiaDepartment of Research and Development, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Sawangi, Wardha 442001, IndiaChronic kidney disease (CKD) is a significant global health challenge that requires timely detection and accurate prognosis for effective treatment and management. The application of machine learning (ML) algorithms for CKD detection and prediction holds promising potential for improving patient outcomes. By incorporating key features which contribute to CKD, these algorithms enhance our ability to identify high-risk individuals and initiate timely interventions. This research highlights the importance of leveraging machine learning techniques to augment existing medical knowledge and improve the identification and management of kidney disease. In this paper, we explore the utilization of diverse ML algorithms, including gradient boost (GB), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), histogram boost (HB), and XGBoost (XGB) to detect and predict chronic kidney disease (CKD). The aim is to improve early detection and prognosis, enhancing patient outcomes and reducing the burden on healthcare systems. We evaluated the performance of the ML algorithms using key metrics like accuracy, precision, recall, and F1 score. Additionally, we conducted feature significance analysis to identify the most influential characteristics in the detection and prediction of kidney disease. The dataset used for training and evaluation contained various clinical and demographic attributes of patients, including serum creatinine level, blood pressure, and age, among others. The proficiency analysis of the ML algorithms revealed consistent predictors across all models, with serum creatinine level, blood pressure, and age emerging as particularly effective in identifying individuals at risk of kidney disease. These findings align with established medical knowledge and emphasize the pivotal role of these attributes in early detection and prognosis. In conclusion, our study demonstrates the effectiveness of diverse machine learning algorithms in detecting and predicting kidney disease. The identification of influential predictors, such as serum creatinine level, blood pressure, and age, underscores their significance in early detection and prognosis. By leveraging machine learning techniques, we can enhance the accuracy and efficiency of kidney disease diagnosis and treatment, ultimately improving patient outcomes and healthcare system effectiveness.https://www.mdpi.com/2075-4418/13/19/3151chronic kidney disease (CKD)prognosismachine learning (ML)gradient boost (GB)decision tree (DT)K-nearest neighbors (KNN) |
| spellingShingle | Yogita Dubey Pranav Mange Yash Barapatre Bhargav Sable Prachi Palsodkar Roshan Umate Unlocking Precision Medicine for Prognosis of Chronic Kidney Disease Using Machine Learning Diagnostics chronic kidney disease (CKD) prognosis machine learning (ML) gradient boost (GB) decision tree (DT) K-nearest neighbors (KNN) |
| title | Unlocking Precision Medicine for Prognosis of Chronic Kidney Disease Using Machine Learning |
| title_full | Unlocking Precision Medicine for Prognosis of Chronic Kidney Disease Using Machine Learning |
| title_fullStr | Unlocking Precision Medicine for Prognosis of Chronic Kidney Disease Using Machine Learning |
| title_full_unstemmed | Unlocking Precision Medicine for Prognosis of Chronic Kidney Disease Using Machine Learning |
| title_short | Unlocking Precision Medicine for Prognosis of Chronic Kidney Disease Using Machine Learning |
| title_sort | unlocking precision medicine for prognosis of chronic kidney disease using machine learning |
| topic | chronic kidney disease (CKD) prognosis machine learning (ML) gradient boost (GB) decision tree (DT) K-nearest neighbors (KNN) |
| url | https://www.mdpi.com/2075-4418/13/19/3151 |
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