Explainable machine learning model for predicting the severity level of chronic kidney disease
Explainable machine learning model for predicting the severity level of chronic kidney disease
| Accessed Date | 2024-12-24T11:27:26Z | |
| Date Availabe | 2024-12-24T11:27:26Z | |
| Issued Date | 2024 | |
| Description | This cross-sectional survey was conducted in Felegehiwot hospital, Kidanemihret Speciality Clinic, and Minilik II Hospital and encompassed data from 2012 up to 2016 data collection sources to gather necessary data (1,325) for prediction. Python software was used for analysis and prediction. We use three steps to deal with missing values in our dataset. for attributes like age, blood pressure, chloride, sodium, potassium, blood urea nitrogen, creatinine, white blood cell count, red blood cell count, hemoglobin, mean cell volume, and platelets. | |
| URI | https://rdmc.aphi.gov.et/handle/123456789/109 | |
| Language | en | |
| Title | Explainable machine learning model for predicting the severity level of chronic kidney disease | |
| Type | Dataset | |
| Entity Type | ||
| Geographic Coverage | Regional | |
| Sex Coverage | Yes | |
| Format | Excel | |
| RDMC ID | APHI-RDMC-056 | |
| Keyword | Chronic Kidney Disease, Explainable Machine Learning, Black-box model, Severity level | |
| Objective | This study aimed to examine the explainable machine learning model for predicting the severity level of chronic kidney disease | |
| Study Population | All documents narrating the CKD in the Felegehiwot Hospital, Kidanemihret Speciality Clinic, and Minilik II Hospital between 2012 up to 2016. |
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