Height prediction for growth hormone deficiency treatment planning using deep learning

Muhammad Ilyas; Jawad Ahmad; Alistair Lawson; Jan Sher Khan; Ahsen Tahir; Ahsan Adeel; Hadi Larijani; Abdelfateh Kerrouche; M. Guftar Shaikh; William Buchanan; Amir Hussain

doi:10.1007/978-3-030-39431-8_8

Height prediction for growth hormone deficiency treatment planning using deep learning

Muhammad Ilyas
, Jawad Ahmad^*
, Alistair Lawson
, Jan Sher Khan
, Ahsen Tahir
, Ahsan Adeel
, Hadi Larijani
, Abdelfateh Kerrouche
, M. Guftar Shaikh
, William Buchanan
, Amir Hussain

^*Corresponding author for this work

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

4 Scopus citations

Abstract

Prospective studies using longitudinal patient data can be used to help to predict responsiveness to Growth Hormone (GH) therapy and assess any suspected risks. In this paper, a novel Clinical Decision Support System (CDSS) is developed to predict growth (in terms of height) in children with Growth Hormone Deficiency (GHD) just before the start of GH therapy. A Deep Feed-Forward Neural Network (DFFNN) model is proposed, developed and evaluated for height prediction with seven input parameters. The essential input parameters to the DFFNN are gender, mother’s height, father’s height, current weight, chronological age, bone age, and GHD. The proposed model is trained using the Levenberg Marquardt (LM) learning algorithm. Experimental results are evaluated and compared for different learning rates. Measures of the quality of the fit of the model such as Root Mean Square (RMSE), Normalized Root Mean Square (N-RMSE), and Mean Absolute Percentage Error (MAPE) show that the proposed deep learning model is robust in terms of accuracy and can effectively predict growth (in terms of height) in children.

Original language	English
Title of host publication	Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings
Editors	Jinchang Ren, Amir Hussain, Huimin Zhao, Jun Cai, Rongjun Chen, Yinyin Xiao, Kaizhu Huang, Jiangbin Zheng
Publisher	Springer
Pages	76-85
Number of pages	10
ISBN (Print)	9783030394301
DOIs	https://doi.org/10.1007/978-3-030-39431-8_8
State	Published - 2020
Externally published	Yes
Event	10th International Conference on Brain Inspired Cognitive Systems, BICS 2019 - Guangzhou, China Duration: 13 Jul 2019 → 14 Jul 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11691 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Brain Inspired Cognitive Systems, BICS 2019
Country/Territory	China
City	Guangzhou
Period	13/07/19 → 14/07/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

Deep learning
Growth Hormone Deficiency
Height prediction
Levenberg Marquardt (LM) learning
Normalized Root Mean Square
Root Mean Square

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-39431-8_8

Cite this

Ilyas, M., Ahmad, J., Lawson, A., Khan, J. S., Tahir, A., Adeel, A., Larijani, H., Kerrouche, A., Shaikh, M. G., Buchanan, W., & Hussain, A. (2020). Height prediction for growth hormone deficiency treatment planning using deep learning. In J. Ren, A. Hussain, H. Zhao, J. Cai, R. Chen, Y. Xiao, K. Huang, & J. Zheng (Eds.), Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings (pp. 76-85). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11691 LNAI). Springer. https://doi.org/10.1007/978-3-030-39431-8_8

Ilyas, Muhammad ; Ahmad, Jawad ; Lawson, Alistair et al. / Height prediction for growth hormone deficiency treatment planning using deep learning. Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. editor / Jinchang Ren ; Amir Hussain ; Huimin Zhao ; Jun Cai ; Rongjun Chen ; Yinyin Xiao ; Kaizhu Huang ; Jiangbin Zheng. Springer, 2020. pp. 76-85 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{9671b10de41e43249491a09ff7196a94,

title = "Height prediction for growth hormone deficiency treatment planning using deep learning",

abstract = "Prospective studies using longitudinal patient data can be used to help to predict responsiveness to Growth Hormone (GH) therapy and assess any suspected risks. In this paper, a novel Clinical Decision Support System (CDSS) is developed to predict growth (in terms of height) in children with Growth Hormone Deficiency (GHD) just before the start of GH therapy. A Deep Feed-Forward Neural Network (DFFNN) model is proposed, developed and evaluated for height prediction with seven input parameters. The essential input parameters to the DFFNN are gender, mother{\textquoteright}s height, father{\textquoteright}s height, current weight, chronological age, bone age, and GHD. The proposed model is trained using the Levenberg Marquardt (LM) learning algorithm. Experimental results are evaluated and compared for different learning rates. Measures of the quality of the fit of the model such as Root Mean Square (RMSE), Normalized Root Mean Square (N-RMSE), and Mean Absolute Percentage Error (MAPE) show that the proposed deep learning model is robust in terms of accuracy and can effectively predict growth (in terms of height) in children.",

keywords = "Deep learning, Growth Hormone Deficiency, Height prediction, Levenberg Marquardt (LM) learning, Normalized Root Mean Square, Root Mean Square",

author = "Muhammad Ilyas and Jawad Ahmad and Alistair Lawson and Khan, \{Jan Sher\} and Ahsen Tahir and Ahsan Adeel and Hadi Larijani and Abdelfateh Kerrouche and Shaikh, \{M. Guftar\} and William Buchanan and Amir Hussain",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019 ; Conference date: 13-07-2019 Through 14-07-2019",

year = "2020",

doi = "10.1007/978-3-030-39431-8\_8",

language = "English",

isbn = "9783030394301",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "76--85",

editor = "Jinchang Ren and Amir Hussain and Huimin Zhao and Jun Cai and Rongjun Chen and Yinyin Xiao and Kaizhu Huang and Jiangbin Zheng",

booktitle = "Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings",

address = "Germany",

}

Ilyas, M, Ahmad, J, Lawson, A, Khan, JS, Tahir, A, Adeel, A, Larijani, H, Kerrouche, A, Shaikh, MG, Buchanan, W & Hussain, A 2020, Height prediction for growth hormone deficiency treatment planning using deep learning. in J Ren, A Hussain, H Zhao, J Cai, R Chen, Y Xiao, K Huang & J Zheng (eds), Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11691 LNAI, Springer, pp. 76-85, 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019, Guangzhou, China, 13/07/19. https://doi.org/10.1007/978-3-030-39431-8_8

Height prediction for growth hormone deficiency treatment planning using deep learning. / Ilyas, Muhammad; Ahmad, Jawad; Lawson, Alistair et al.
Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. ed. / Jinchang Ren; Amir Hussain; Huimin Zhao; Jun Cai; Rongjun Chen; Yinyin Xiao; Kaizhu Huang; Jiangbin Zheng. Springer, 2020. p. 76-85 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11691 LNAI).

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

TY - GEN

T1 - Height prediction for growth hormone deficiency treatment planning using deep learning

AU - Ilyas, Muhammad

AU - Ahmad, Jawad

AU - Lawson, Alistair

AU - Khan, Jan Sher

AU - Tahir, Ahsen

AU - Adeel, Ahsan

AU - Larijani, Hadi

AU - Kerrouche, Abdelfateh

AU - Shaikh, M. Guftar

AU - Buchanan, William

AU - Hussain, Amir

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - Prospective studies using longitudinal patient data can be used to help to predict responsiveness to Growth Hormone (GH) therapy and assess any suspected risks. In this paper, a novel Clinical Decision Support System (CDSS) is developed to predict growth (in terms of height) in children with Growth Hormone Deficiency (GHD) just before the start of GH therapy. A Deep Feed-Forward Neural Network (DFFNN) model is proposed, developed and evaluated for height prediction with seven input parameters. The essential input parameters to the DFFNN are gender, mother’s height, father’s height, current weight, chronological age, bone age, and GHD. The proposed model is trained using the Levenberg Marquardt (LM) learning algorithm. Experimental results are evaluated and compared for different learning rates. Measures of the quality of the fit of the model such as Root Mean Square (RMSE), Normalized Root Mean Square (N-RMSE), and Mean Absolute Percentage Error (MAPE) show that the proposed deep learning model is robust in terms of accuracy and can effectively predict growth (in terms of height) in children.

AB - Prospective studies using longitudinal patient data can be used to help to predict responsiveness to Growth Hormone (GH) therapy and assess any suspected risks. In this paper, a novel Clinical Decision Support System (CDSS) is developed to predict growth (in terms of height) in children with Growth Hormone Deficiency (GHD) just before the start of GH therapy. A Deep Feed-Forward Neural Network (DFFNN) model is proposed, developed and evaluated for height prediction with seven input parameters. The essential input parameters to the DFFNN are gender, mother’s height, father’s height, current weight, chronological age, bone age, and GHD. The proposed model is trained using the Levenberg Marquardt (LM) learning algorithm. Experimental results are evaluated and compared for different learning rates. Measures of the quality of the fit of the model such as Root Mean Square (RMSE), Normalized Root Mean Square (N-RMSE), and Mean Absolute Percentage Error (MAPE) show that the proposed deep learning model is robust in terms of accuracy and can effectively predict growth (in terms of height) in children.

KW - Deep learning

KW - Growth Hormone Deficiency

KW - Height prediction

KW - Levenberg Marquardt (LM) learning

KW - Normalized Root Mean Square

KW - Root Mean Square

UR - https://www.scopus.com/pages/publications/85080904135

U2 - 10.1007/978-3-030-39431-8_8

DO - 10.1007/978-3-030-39431-8_8

M3 - Conference contribution

AN - SCOPUS:85080904135

SN - 9783030394301

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 76

EP - 85

BT - Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings

A2 - Ren, Jinchang

A2 - Hussain, Amir

A2 - Zhao, Huimin

A2 - Cai, Jun

A2 - Chen, Rongjun

A2 - Xiao, Yinyin

A2 - Huang, Kaizhu

A2 - Zheng, Jiangbin

PB - Springer

T2 - 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019

Y2 - 13 July 2019 through 14 July 2019

ER -

Ilyas M, Ahmad J, Lawson A, Khan JS, Tahir A, Adeel A et al. Height prediction for growth hormone deficiency treatment planning using deep learning. In Ren J, Hussain A, Zhao H, Cai J, Chen R, Xiao Y, Huang K, Zheng J, editors, Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. Springer. 2020. p. 76-85. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-39431-8_8

Height prediction for growth hormone deficiency treatment planning using deep learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this