Chronic Kidney Disease Prediction Using Machine Learning
| dc.contributor.advisor | Dr. Ashrafuzzaman Khan | |
| dc.contributor.author | Md Gulam Rahman | |
| dc.contributor.author | Joy Chandra Saha | |
| dc.contributor.author | Bishal Bhowmik | |
| dc.contributor.author | Fairooz Nawar | |
| dc.contributor.id | 1831112042 | |
| dc.contributor.id | 1831563042 | |
| dc.contributor.id | 1831047042 | |
| dc.contributor.id | 1831244042 | |
| dc.coverage.department | Electrical and Computer Engineering | |
| dc.date.accessioned | 2025 | |
| dc.date.accessioned | 2025-07-21T09:32:20Z | |
| dc.date.available | 2025-07-21T09:32:20Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Chronic Kidney Disease (CKD) is a widespread and serious global health issue affecting more than 10% of the population. Its progression often goes unnoticed until late stages due to slow development and minimal symptoms. This work aims to predict CKD using various machine learning models, such as logistic regression, K Nearest Neighbor, and Decision tree algorithms. The document presents related research, system design, impact assessment, ethical considerations, tools employed, and concludes with results and insights. Early detection through machine learning can significantly improve preventive measures and overall health outcomes. | |
| dc.description.degree | Undergraduate | |
| dc.identifier.cd | 600000197 | |
| dc.identifier.print-thesis | To be assigned | |
| dc.identifier.uri | https://repository.northsouth.edu/handle/123456789/1298 | |
| dc.language.iso | en | |
| dc.publisher | North South University | |
| dc.rights | ©NSU Library | |
| dc.title | Chronic Kidney Disease Prediction Using Machine Learning | |
| oaire.citation.endPage | 30 | |
| oaire.citation.startPage | 1 |
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