Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8996
Title: Analysing SEER cancer data using signed maximal frequent itemset networks
Authors: Kocak, Yunuscan
Ozyer, Tansel
Keywords: cancer data analysis
frequent pattern mining
machine learning
network analysis
signed networks
maximal frequent itemsets
feature selection
lung cancer
pancreatic cancer
Classification
Prediction
Issue Date: 2021
Publisher: Inderscience Enterprises Ltd
Abstract: Evaluating patient prognosis is prominent for predicting the effects and consequences of diseases. Systems can find interesting properties within a data set and predict unseen cases. Feature extraction and feature selection are the critical steps. In this work, a novel network-based feature extraction method is presented and tested on two cancer cases, namely (1) lung and bronchus cancer and (2) pancreatic cancer. Named as Signed Maximal Frequent Itemset Network, the proposed method uses maximal frequent itemsets as actors in a network and extracts features by considering their co-occurrence and structure of the sub-graph. To investigate patterns on prediction, the top ten maximal itemsets are selected with the recursive feature elimination method and their distributions are analysed. In conclusion, survival months are low when the information on the disease was unknown or blank, and higher in case chemotherapy was given and the primary site was labelled, such as head of the pancreas.
URI: https://doi.org/10.1504/IJDMB.2021.124106
https://hdl.handle.net/20.500.11851/8996
ISSN: 1748-5673
1748-5681
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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