| dc.description.abstract | The aim of this study was to test the use of machine learning in early detection of Chronic
Kidney Disease (CKD). The main problem was that CKD usually progresses slowly
without any symptoms and in many cases the patient does not realize it in the early stages.
Therefore, timely and accurate screening requires machine learning models that can
differentiate between healthy and unhealthy patients. In this thesis, we used four machine
learning classifiers - logistic regression, decision tree, random forest, and KNN. Each
model was trained with the same dataset and preprocessing, which solved missing values
and other problems. The tests showed that random forest performed the best, giving
completely correct results (in accuracy, precision, recall, and F1-score). However, other
models also gave good results, of which logistic regression and decision tree are
particularly noteworthy. These results show that machine learning can improve the
accuracy of CKD detection and can help improve the clinical screening process. In the
future, studies with larger and more diverse datasets can be conducted to further improve
the performance of the model. | en_US |
