Uji Akurasi Dataset Pasien Pasca Operasi Menggunakan Algoritma Naïve Bayes Menggunakan Weka Tools
DOI:
https://doi.org/10.30865/jurikom.v7i1.1761Keywords:
Data Mining, Algorithm, Naïve Bayes, Post-Operative, ExperimenterAbstract
Postoperative patient data sets taken for testing of this data are sourced from the UCI repository on the website https://archive.ics.uci.edu/ml/datasets/Post-Operative+Patient. Based on the website address, the study was conducted by Sharon Summers, School of Nursing, University of Kansas, Medical Center, Kansas City, KS 66160 and Linda Woolery, School of Nursing, University of Missouri, Columbia, MO 6521. Number of attributes from this data set there are 8 and 1 class, the attributes in question include; L-CORE (patient's internal temperature in C), L-SURF (patient's surface temperature in C), L-O2 (oxygen saturation in%), L-BP (last measurement of blood pressure), SURF-STBL (stability of the patient's surface temperature ), CORE-STBL (stability of the patient), BP-STBL (stability of the patient's blood pressure), COMFORT (perceived comfort of the patient at discharge, measured as an integer between 0 and 20) and ADM-DECS decision class / patient exit decision with information (I = patient sent to intensive care unit, S = patient ready to go home, A = patient sent to general hospital floor).
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