@article{article_281134, title={Decision Tree Application for Renal Calculi Diagnosis}, journal={International Journal of Applied Mathematics Electronics and Computers}, pages={404–407}, year={2016}, DOI={10.18100/ijamec.281134}, url={https://izlik.org/JA69AN84AC}, author={Topaloğlu, Murat and Malkoç, Gözde}, keywords={Data Mining,Decision Tree,Renal Calculi Diagnosis}, abstract={<p> <span style="font-size:12pt;font-family:’Times New Roman’, serif;color:#000000;" lang="en-us" xml:lang="en-us">Data mining is used for the extraction of secret, valuable and usable
data from the big data and to provide strategic decision support. It created a
new perspective for the use of the data in healthcare in addition to finding
the answers of unexplored questions. It has gained wider usage as a method. The
aim of this study is to develop a decision tree and a list of rules by data
mining for the early diagnosis of renal calculi. A data set including blind and
retrospective data for 150 people can diagnose with 6 attributes. A decision
support system analysis was developed for the diagnosis of the patients with
suspected renal calculi. Based on the results obtained and the analysis
developed, a decision tree and list of rules were created to determine the factors
that affect renal calculi. Weka program and J48 algorithm were used to create
the decision tree and the list of rules and it was found to be </span> <span style="font-size:12pt;font-family:’Times New Roman’, serif;color:#000000;" lang="en-us" xml:lang="en-us">74.63% successful. </span> <br /> </p>}, number={Special Issue-1}