Liver Patient Classification using Logistic Regression

Syed Hasan Adil; Mansoor Ebrahim; Kamran Raza; Syed Saad Azhar Ali; Manzoor Ahmed Hashmani

doi:10.1109/ICCOINS.2018.8510581

Liver Patient Classification using Logistic Regression

Syed Hasan Adil^*, Mansoor Ebrahim, Kamran Raza, Syed Saad Azhar Ali, Manzoor Ahmed Hashmani

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

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.

Original language	English
Title of host publication	2018 4th International Conference on Computer and Information Sciences
Subtitle of host publication	Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538647431
DOIs	https://doi.org/10.1109/ICCOINS.2018.8510581
State	Published - 25 Oct 2018
Externally published	Yes
Event	4th International Conference on Computer and Information Sciences, ICCOINS 2018 - Kuala Lumpur, Malaysia Duration: 13 Aug 2018 → 14 Aug 2018

Publication series

Name	2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings

Conference

Conference	4th International Conference on Computer and Information Sciences, ICCOINS 2018
Country/Territory	Malaysia
City	Kuala Lumpur
Period	13/08/18 → 14/08/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Classification
Liver Disease
Logistic Regression
Python
sklearn

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems and Management
Renewable Energy, Sustainability and the Environment

Access to Document

10.1109/ICCOINS.2018.8510581

Cite this

Adil, S. H., Ebrahim, M., Raza, K., Azhar Ali, S. S., & Ahmed Hashmani, M. (2018). Liver Patient Classification using Logistic Regression. In 2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings Article 8510581 (2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCOINS.2018.8510581

Adil, Syed Hasan ; Ebrahim, Mansoor ; Raza, Kamran et al. / Liver Patient Classification using Logistic Regression. 2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings).

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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{\"i}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.",

keywords = "Classification, Liver Disease, Logistic Regression, Python, sklearn",

author = "Adil, \{Syed Hasan\} and Mansoor Ebrahim and Kamran Raza and \{Azhar Ali\}, \{Syed Saad\} and \{Ahmed Hashmani\}, Manzoor",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 4th International Conference on Computer and Information Sciences, ICCOINS 2018 ; Conference date: 13-08-2018 Through 14-08-2018",

year = "2018",

month = oct,

day = "25",

doi = "10.1109/ICCOINS.2018.8510581",

language = "English",

series = "2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings",

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Adil, SH, Ebrahim, M, Raza, K, Azhar Ali, SS & Ahmed Hashmani, M 2018, Liver Patient Classification using Logistic Regression. in 2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings., 8510581, 2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 4th International Conference on Computer and Information Sciences, ICCOINS 2018, Kuala Lumpur, Malaysia, 13/08/18. https://doi.org/10.1109/ICCOINS.2018.8510581

Liver Patient Classification using Logistic Regression. / Adil, Syed Hasan; Ebrahim, Mansoor; Raza, Kamran et al.
2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8510581 (2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Liver Patient Classification using Logistic Regression

AU - Adil, Syed Hasan

AU - Ebrahim, Mansoor

AU - Raza, Kamran

AU - Azhar Ali, Syed Saad

AU - Ahmed Hashmani, Manzoor

PY - 2018/10/25

Y1 - 2018/10/25

N2 - 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.

AB - 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.

KW - Classification

KW - Liver Disease

KW - Logistic Regression

KW - Python

KW - sklearn

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U2 - 10.1109/ICCOINS.2018.8510581

DO - 10.1109/ICCOINS.2018.8510581

M3 - Conference contribution

AN - SCOPUS:85057078574

T3 - 2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings

BT - 2018 4th International Conference on Computer and Information Sciences

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th International Conference on Computer and Information Sciences, ICCOINS 2018

Y2 - 13 August 2018 through 14 August 2018

ER -

Adil SH, Ebrahim M, Raza K, Azhar Ali SS, Ahmed Hashmani M. Liver Patient Classification using Logistic Regression. In 2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8510581. (2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings). doi: 10.1109/ICCOINS.2018.8510581

Liver Patient Classification using Logistic Regression

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this