Liver Patient Classification using Logistic Regression

Adil, S.H. and Ebrahim, M. and Raza, K. and Azhar Ali, S.S. and Ahmed Hashmani, M. (2018) Liver Patient Classification using Logistic Regression. In: UNSPECIFIED.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

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 (Naïve 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. © 2018 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: 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
Uncontrolled 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)
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 16:36
Last Modified: 09 Nov 2023 16:36
URI: https://khub.utp.edu.my/scholars/id/eprint/9866

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