{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T12:41:54Z","timestamp":1754397714358,"version":"3.28.0"},"reference-count":31,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,10]]},"DOI":"10.1109\/bigdata50022.2020.9377903","type":"proceedings-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T17:10:21Z","timestamp":1616173821000},"page":"3786-3792","source":"Crossref","is-referenced-by-count":6,"title":["Features Importance to Improve Interpretability of Chronic Kidney Disease Early Diagnosis"],"prefix":"10.1109","author":[{"given":"Pedro A.","family":"Moreno-Sanchez","sequence":"first","affiliation":[]}],"member":"263","reference":[{"journal-title":"Interpretable Machine Learning","year":"2020","author":"molnar","key":"ref31"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.03.013"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLC.2012.6359637"},{"key":"ref11","article-title":"Statistical analysis and predicting kidney diseases using machine learning algorithms","volume":"4","author":"baby","year":"2015","journal-title":"International Journal of Engineering Research and Technology"},{"key":"ref12","first-page":"242","article-title":"Performance comparison of three data mining techniques for predicting kidney dialysis survivability","volume":"7","author":"lakshmi","year":"2014","journal-title":"International Journal of Advances in Engineering & Technology"},{"journal-title":"UCI Machine Learning Repository","year":"2017","author":"dua","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CIET.2018.8660844"},{"key":"ref15","first-page":"49","article-title":"Generating comparative analysis of early stage prediction of Chronic Kidney Disease","volume":"5","author":"rubini","year":"2015","journal-title":"International Journal of Modern Engineering Research (IJMER)"},{"key":"ref16","first-page":"544","article-title":"Detection of chronic kidney disease by using ensemble classifiers","author":"basar","year":"2017","journal-title":"2017 Electronics ELECTRONICS"},{"article-title":"Prediction of Chronic Kidney Disease Using Random Forest Machine Learning Algorithm","year":"2016","author":"eyck","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICACCI.2016.7732224"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CCCS.2015.7374193"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/MCSE.2007.58"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/sj.ki.5002009"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-009-9153-8"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1001\/archinte.164.6.659"},{"key":"ref6","first-page":"18","article-title":"Chronic kidney disease: a new classification and staging system","volume":"39","author":"perazella","year":"2003","journal-title":"Hospital Physician"},{"key":"ref29","first-page":"6","article-title":"Scikit-learn: Machine Learning in Python","author":"pedregosa","year":"0","journal-title":"Machine Learning in Python"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1155\/2012\/691369"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3152723.3152724"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI.2016.36"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30045-3"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.5815\/ijitcs.2012.07.03"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s11255-016-1346-4"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CONFLUENCE.2016.7508132"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-5953-8_34"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CITSM.2017.8089245"},{"article-title":"Fused Features Classification for the Effective Prediction of Chronic Kidney Disease","year":"2016","author":"mohammedsiyad","key":"ref24"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MITICON.2016.8025242"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-31204-0_9"},{"key":"ref25","first-page":"11","article-title":"CRISP-DM: Towards a Standard Process Model for Data Mining","author":"wirth","year":"0"}],"event":{"name":"2020 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2020,12,10]]},"location":"Atlanta, GA, USA","end":{"date-parts":[[2020,12,13]]}},"container-title":["2020 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9377717\/9377728\/09377903.pdf?arnumber=9377903","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T11:46:49Z","timestamp":1656330409000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9377903\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,10]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/bigdata50022.2020.9377903","relation":{},"subject":[],"published":{"date-parts":[[2020,12,10]]}}}