Cancer Incidence Prediction Using a Hybrid Model of Wavelet Transform and LSTM Networks

Amani Alrobai; Musfira Jilani

doi:10.1007/978-3-030-36365-9_19

Cancer Incidence Prediction Using a Hybrid Model of Wavelet Transform and LSTM Networks

Amani Alrobai
, Musfira Jilani

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

Abstract

Cancer is a major public health concern. Being able to predict the number of future cancer incidences is vital to allocate appropriate healthcare resources and research funding. Real data, such as cancer incidences, exhibit non-linear characteristics along with a high degree of fluctuations, which makes the modelling process difficult. This study explores the potential of time series modelling, especially the long short-term memory (LSTM) recurrent neural networks, to predict the number of cancer incidences. A novel hybrid model of the wavelet transform and LSTM is proposed with the goal of increasing forecasting accuracy. The evaluation of the proposed models for the three most common types of cancer in the Kingdom of Saudi Arabia shows that the proposed hybrid model has better accuracy than the original LSTM model.

Original language	English
Title of host publication	Advances in Data Science, Cyber Security and IT Applications - 1st International Conference on Computing, ICC 2019, Proceedings
Editors	Auhood Alfaries, Hanan Mengash, Ansar Yasar, Elhadi Shakshuki
Publisher	Springer
Pages	224-235
Number of pages	12
ISBN (Print)	9783030363642
DOIs	https://doi.org/10.1007/978-3-030-36365-9_19
Publication status	Published - 2019
Event	1st International Conference on Intelligent Cloud Computing, ICC 2019 - Riyadh, Saudi Arabia Duration: 10 Dec 2019 → 12 Dec 2019

Publication series

Name	Communications in Computer and Information Science
Volume	1097 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	1st International Conference on Intelligent Cloud Computing, ICC 2019
Country/Territory	Saudi Arabia
City	Riyadh
Period	10/12/19 → 12/12/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Long short-term memory
Time series prediction
Wavelet transform

Access to Document

10.1007/978-3-030-36365-9_19

Cite this

Alrobai, A., & Jilani, M. (2019). Cancer Incidence Prediction Using a Hybrid Model of Wavelet Transform and LSTM Networks. In A. Alfaries, H. Mengash, A. Yasar, & E. Shakshuki (Eds.), Advances in Data Science, Cyber Security and IT Applications - 1st International Conference on Computing, ICC 2019, Proceedings (pp. 224-235). (Communications in Computer and Information Science; Vol. 1097 CCIS). Springer. https://doi.org/10.1007/978-3-030-36365-9_19

Alrobai, Amani ; Jilani, Musfira. / Cancer Incidence Prediction Using a Hybrid Model of Wavelet Transform and LSTM Networks. Advances in Data Science, Cyber Security and IT Applications - 1st International Conference on Computing, ICC 2019, Proceedings. editor / Auhood Alfaries ; Hanan Mengash ; Ansar Yasar ; Elhadi Shakshuki. Springer, 2019. pp. 224-235 (Communications in Computer and Information Science).

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title = "Cancer Incidence Prediction Using a Hybrid Model of Wavelet Transform and LSTM Networks",

abstract = "Cancer is a major public health concern. Being able to predict the number of future cancer incidences is vital to allocate appropriate healthcare resources and research funding. Real data, such as cancer incidences, exhibit non-linear characteristics along with a high degree of fluctuations, which makes the modelling process difficult. This study explores the potential of time series modelling, especially the long short-term memory (LSTM) recurrent neural networks, to predict the number of cancer incidences. A novel hybrid model of the wavelet transform and LSTM is proposed with the goal of increasing forecasting accuracy. The evaluation of the proposed models for the three most common types of cancer in the Kingdom of Saudi Arabia shows that the proposed hybrid model has better accuracy than the original LSTM model.",

keywords = "Long short-term memory, Time series prediction, Wavelet transform",

author = "Amani Alrobai and Musfira Jilani",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 1st International Conference on Intelligent Cloud Computing, ICC 2019 ; Conference date: 10-12-2019 Through 12-12-2019",

year = "2019",

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language = "English",

isbn = "9783030363642",

series = "Communications in Computer and Information Science",

publisher = "Springer",

pages = "224--235",

editor = "Auhood Alfaries and Hanan Mengash and Ansar Yasar and Elhadi Shakshuki",

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Alrobai, A & Jilani, M 2019, Cancer Incidence Prediction Using a Hybrid Model of Wavelet Transform and LSTM Networks. in A Alfaries, H Mengash, A Yasar & E Shakshuki (eds), Advances in Data Science, Cyber Security and IT Applications - 1st International Conference on Computing, ICC 2019, Proceedings. Communications in Computer and Information Science, vol. 1097 CCIS, Springer, pp. 224-235, 1st International Conference on Intelligent Cloud Computing, ICC 2019, Riyadh, Saudi Arabia, 10/12/19. https://doi.org/10.1007/978-3-030-36365-9_19

Cancer Incidence Prediction Using a Hybrid Model of Wavelet Transform and LSTM Networks. / Alrobai, Amani; Jilani, Musfira.
Advances in Data Science, Cyber Security and IT Applications - 1st International Conference on Computing, ICC 2019, Proceedings. ed. / Auhood Alfaries; Hanan Mengash; Ansar Yasar; Elhadi Shakshuki. Springer, 2019. p. 224-235 (Communications in Computer and Information Science; Vol. 1097 CCIS).

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

TY - GEN

T1 - Cancer Incidence Prediction Using a Hybrid Model of Wavelet Transform and LSTM Networks

AU - Alrobai, Amani

AU - Jilani, Musfira

PY - 2019

Y1 - 2019

N2 - Cancer is a major public health concern. Being able to predict the number of future cancer incidences is vital to allocate appropriate healthcare resources and research funding. Real data, such as cancer incidences, exhibit non-linear characteristics along with a high degree of fluctuations, which makes the modelling process difficult. This study explores the potential of time series modelling, especially the long short-term memory (LSTM) recurrent neural networks, to predict the number of cancer incidences. A novel hybrid model of the wavelet transform and LSTM is proposed with the goal of increasing forecasting accuracy. The evaluation of the proposed models for the three most common types of cancer in the Kingdom of Saudi Arabia shows that the proposed hybrid model has better accuracy than the original LSTM model.

AB - Cancer is a major public health concern. Being able to predict the number of future cancer incidences is vital to allocate appropriate healthcare resources and research funding. Real data, such as cancer incidences, exhibit non-linear characteristics along with a high degree of fluctuations, which makes the modelling process difficult. This study explores the potential of time series modelling, especially the long short-term memory (LSTM) recurrent neural networks, to predict the number of cancer incidences. A novel hybrid model of the wavelet transform and LSTM is proposed with the goal of increasing forecasting accuracy. The evaluation of the proposed models for the three most common types of cancer in the Kingdom of Saudi Arabia shows that the proposed hybrid model has better accuracy than the original LSTM model.

KW - Long short-term memory

KW - Time series prediction

KW - Wavelet transform

UR - https://www.scopus.com/pages/publications/85087149540

U2 - 10.1007/978-3-030-36365-9_19

DO - 10.1007/978-3-030-36365-9_19

M3 - Conference contribution

AN - SCOPUS:85087149540

SN - 9783030363642

T3 - Communications in Computer and Information Science

SP - 224

EP - 235

BT - Advances in Data Science, Cyber Security and IT Applications - 1st International Conference on Computing, ICC 2019, Proceedings

A2 - Alfaries, Auhood

A2 - Mengash, Hanan

A2 - Yasar, Ansar

A2 - Shakshuki, Elhadi

PB - Springer

T2 - 1st International Conference on Intelligent Cloud Computing, ICC 2019

Y2 - 10 December 2019 through 12 December 2019

ER -

Alrobai A, Jilani M. Cancer Incidence Prediction Using a Hybrid Model of Wavelet Transform and LSTM Networks. In Alfaries A, Mengash H, Yasar A, Shakshuki E, editors, Advances in Data Science, Cyber Security and IT Applications - 1st International Conference on Computing, ICC 2019, Proceedings. Springer. 2019. p. 224-235. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-36365-9_19

Cancer Incidence Prediction Using a Hybrid Model of Wavelet Transform and LSTM Networks

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this