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Erschienen in:
Hybrid Data Mining to Reduce False Positive and False Negative Prediction in Intrusion Detection System Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

A Prediction Survival Model Based on Support Vector Machine and Extreme Learning Machine for Colorectal Cancer

verfasst von : Preeti, Rajni Bala, Ram Pal Singh

Erschienen in: Advances in Information and Communication Networks

Verlag: Springer International Publishing

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Abstract

Colorectal cancer is the third largest cause of cancer deaths in men and second most common in women worldwide. In this paper, a prediction model based on Support Vector Machine (SVM) and Extreme Learning Machine (ELM) combined with feature selection has been developed to estimate colorectal-cancer-specific survival after 5 years of diagnosis. Experiments have been conducted on dataset of Colorectal Cancer patients publicly available from Surveillance, Epidemiology, and End Results (SEER) program. The performance measures used to evaluate proposed methods are classification accuracy, F-score, sensitivity, specificity, positive and negative predictive values and receiver operating characteristic (ROC) curves. The results show very good classification accuracy for 5-year survival prediction for the SVM and ELM model with 80%–20% partition of data with 16 number of features and this is very promising as compared to existing learning models result.

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Vorheriges Kapitel A Generic Multi-modal Dynamic Gesture Recognition System Using Machine Learning
Nächstes Kapitel Sentiment Classification of Customer’s Reviews About Automobiles in Roman Urdu
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Metadaten
Titel
A Prediction Survival Model Based on Support Vector Machine and Extreme Learning Machine for Colorectal Cancer
verfasst von
Preeti
Rajni Bala
Ram Pal Singh
Copyright-Jahr
2019
Buch
Advances in Information and Communication Networks

Print ISBN: 978-3-030-03404-7

Electronic ISBN: 978-3-030-03405-4

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-03405-4_43

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