Comparison of artificial neural networks with logistic regression in prediction of kidney transplant outcomes

Fariba Shadabi, Dharmendra Sharma

Research output: A Conference proceeding or a Chapter in BookConference contribution

5 Citations (Scopus)

Abstract

Predicting the outcome of a graft transplant with high level of accuracy is a challenging task. To answer the challenge, data mining can play a significant role. The goal of this study is to compare the performances and features of an Artificially Intelligent (AI)-based data mining technique namely Artificial Neural Network with Logistic Regression as a standard statistical data mining method to predict the outcome of kidney transplants over a 2-year horizon. The methodology employed utilizes a dataset made available to us from a kidney transplant database. The dataset embodies a number of important properties, which make it a good starting point for the purpose of this research. Results reveal that in most cases, the neural network technique outperforms logistic regression. This study highlights that in some situations, different techniques can potentially be integrated to improve the accuracy of predictions.

Original languageEnglish
Title of host publicationProceedings International Conference on Future Computer and Communication 2009
Subtitle of host publicationICFCC 2009
Place of PublicationDanvers, USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages543-547
Number of pages5
ISBN (Print)9780769535913
DOIs
Publication statusPublished - 2009
Event2009 International Conference on Future Computer and Communication, ICFCC 2009 - Kuala Lumpar, Malaysia
Duration: 3 Apr 20095 Apr 2009

Conference

Conference2009 International Conference on Future Computer and Communication, ICFCC 2009
CountryMalaysia
CityKuala Lumpar
Period3/04/095/04/09

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Shadabi, F., & Sharma, D. (2009). Comparison of artificial neural networks with logistic regression in prediction of kidney transplant outcomes. In Proceedings International Conference on Future Computer and Communication 2009: ICFCC 2009 (pp. 543-547). Danvers, USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICFCC.2009.139