@inproceedings{scholars9866,
            year = {2018},
             doi = {10.1109/ICCOINS.2018.8510581},
            note = {cited By 8; Conference of 4th International Conference on Computer and Information Sciences, ICCOINS 2018 ; Conference Date: 13 August 2018 Through 14 August 2018; Conference Code:141665},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings},
           title = {Liver Patient Classification using Logistic Regression},
        abstract = {In this research paper, we have applied machine learning approach to classify liver patient (i.e., Liver Patient or Not Liver Patient) using patient gender and laboratory medical test data. The labelled dataset was published on UCI machine learning repository as "Indian Liver Patient Records". The motivation behind this work is to apply simple and less computational classification technique like Logistic Regression and compare its results with earlier results obtained on the same dataset by other researchers. The classification results of Logistic regression have proved its significance on this dataset by achieving better classification accuracy than NBC (Na{\~A}?ve Bayes Classifier), C4.5 (Decision Tree), SVM (Support Vector Machine), ANN (Artificial Neural Network), and KNN (K Nearest Neighbors) as presented in Ramana et al., research paper. {\^A}{\copyright} 2018 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057078574&doi=10.1109\%2fICCOINS.2018.8510581&partnerID=40&md5=d20869dec47e2f9cee34be40bdbea6e8},
        keywords = {Barium compounds; Decision trees; Nearest neighbor search; Neural networks; Regression analysis; Sodium compounds; Support vector machines, ANN (artificial neural network); Classification technique; Liver disease; Logistic regressions; Python; sklearn; SVM(support vector machine); UCI machine learning repository, Classification (of information)},
            isbn = {9781538647431},
          author = {Adil, S. H. and Ebrahim, M. and Raza, K. and Azhar Ali, S. S. and Ahmed Hashmani, M.}
}