Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements
Chronic kidney disease (CKD) is a global health concern with early detection playing a pivotal role in effective management. Machine learning models demonstrate promise in CKD detection, yet the impact on detection and classification using different sets of clinical features remains under-explored....
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Nature Research
2025
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| _version_ | 1824459260803678208 |
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| author | Metherall, B Berryman, AK Brennan, GS |
| author_facet | Metherall, B Berryman, AK Brennan, GS |
| author_sort | Metherall, B |
| collection | OXFORD |
| description | Chronic kidney disease (CKD) is a global health concern with early detection playing a pivotal role in effective management. Machine learning models demonstrate promise in CKD detection, yet the impact on detection and classification using different sets of clinical features remains under-explored. In this study, we focus on CKD classification and creatinine prediction using three sets of features: at-home, monitoring, and laboratory. We employ artificial neural networks (ANNs) and random forests (RFs) on a dataset of 400 patients with 25 input features, which we divide into three feature sets. Using 10-fold cross-validation, we calculate metrics such as accuracy, true positive rate (TPR), true negative rate (TNR), and mean squared error. Our results reveal RF achieves superior accuracy (92.5%) in at-home CKD classification over ANNs (82.9%). ANNs achieve a higher TPR (92.0%), but a lower TNR (67.9%) compared with RFs (90.0% and 95.8%, respectively). For monitoring and laboratory features, both methods achieve accuracies exceeding 98%. The R2 score for creatinine regression is approximately 0.3 higher with laboratory features than at-home features. Feature importance analysis identifies the key clinical variables hemoglobin and blood urea, and key comorbidities hypertension and diabetes mellitus, in agreement with previous studies. Machine learning models, particularly RFs, exhibit promise in CKD diagnosis and highlight significant features in CKD detection. Moreover, such models may assist in screening a general population using at-home features—potentially increasing early detection of CKD, thus improving patient care and offering hope for a more effective approach to managing this prevalent health condition. |
| first_indexed | 2025-02-19T04:38:58Z |
| format | Journal article |
| id | oxford-uuid:12705667-89e0-46dd-b8d2-205622ecadcc |
| institution | University of Oxford |
| language | English |
| last_indexed | 2025-02-19T04:38:58Z |
| publishDate | 2025 |
| publisher | Nature Research |
| record_format | dspace |
| spelling | oxford-uuid:12705667-89e0-46dd-b8d2-205622ecadcc2025-02-14T20:04:17ZMachine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurementsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:12705667-89e0-46dd-b8d2-205622ecadccEnglishJisc Publications RouterNature Research2025Metherall, BBerryman, AKBrennan, GSChronic kidney disease (CKD) is a global health concern with early detection playing a pivotal role in effective management. Machine learning models demonstrate promise in CKD detection, yet the impact on detection and classification using different sets of clinical features remains under-explored. In this study, we focus on CKD classification and creatinine prediction using three sets of features: at-home, monitoring, and laboratory. We employ artificial neural networks (ANNs) and random forests (RFs) on a dataset of 400 patients with 25 input features, which we divide into three feature sets. Using 10-fold cross-validation, we calculate metrics such as accuracy, true positive rate (TPR), true negative rate (TNR), and mean squared error. Our results reveal RF achieves superior accuracy (92.5%) in at-home CKD classification over ANNs (82.9%). ANNs achieve a higher TPR (92.0%), but a lower TNR (67.9%) compared with RFs (90.0% and 95.8%, respectively). For monitoring and laboratory features, both methods achieve accuracies exceeding 98%. The R2 score for creatinine regression is approximately 0.3 higher with laboratory features than at-home features. Feature importance analysis identifies the key clinical variables hemoglobin and blood urea, and key comorbidities hypertension and diabetes mellitus, in agreement with previous studies. Machine learning models, particularly RFs, exhibit promise in CKD diagnosis and highlight significant features in CKD detection. Moreover, such models may assist in screening a general population using at-home features—potentially increasing early detection of CKD, thus improving patient care and offering hope for a more effective approach to managing this prevalent health condition. |
| spellingShingle | Metherall, B Berryman, AK Brennan, GS Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements |
| title | Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements |
| title_full | Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements |
| title_fullStr | Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements |
| title_full_unstemmed | Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements |
| title_short | Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements |
| title_sort | machine learning for classifying chronic kidney disease and predicting creatinine levels using at home measurements |
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