Diagnosis of Chronic Kidney Disease by Using Random Forest
2017 (Swedish)Conference paper (Refereed)
Chronic kidney disease (CKD) is a global public health problem, affecting approximately 10% of the population worldwide. Yet, there is little direct evidence on how CKD can be diagnosed in a systematic and automatic manner. This paper investigates how CKD can be diagnosed by using machine learning (ML) techniques. ML algorithms have been a driving force in detection of abnormalities in different physiological data, and are, with a great success, employed in different classification tasks. In the present study, a number of different ML classifiers are experimentally validated to a real data set, taken from the UCI Machine Learning Repository, and our findings are compared with the findings reported in the recent literature. The results are quantitatively and qualitatively discussed and our findings reveal that the random forest (RF) classifier achieves the near-optimal performances on the identification of CKD subjects. Hence, we show that ML algorithms serve important function in diagnosis of CKD, with satisfactory robustness, and our findings suggest that RF can also be utilized for the diagnosis of similar diseases.
Place, publisher, year, edition, pages
2017. 589-594 p.
Chronic kidney disease (CKD), Machine learning, Artificial Neural Networks (ANNs), Support Vector Machines (SVM), k-Nearest Neighbour (k-NN), C4.5 Decision Tree Random Forest (RF)
Other Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:liu:diva-135782DOI: 10.1007/978-981-10-4166-2_89OAI: oai:DiVA.org:liu-135782DiVA: diva2:1083694
2nd Conference of Medical and Biological Engineering in Bosnia and Herzegovina (CMBEBIH 2017) , Sarajevo, March 16-18, 2017