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 proceedingConference contribution

2 Citations (Scopus)

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 languageEnglish
Title of host publicationAdvances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings
EditorsJinchang Ren, Amir Hussain, Huimin Zhao, Jun Cai, Rongjun Chen, Yinyin Xiao, Kaizhu Huang, Jiangbin Zheng
PublisherSpringer
Pages76-85
Number of pages10
ISBN (Electronic)9783030394318
ISBN (Print)9783030394301
DOIs
Publication statusPublished - 1 Feb 2020
Event10th International Conference on Brain Inspired Cognitive Systems - Guangdong Polytechnic Normal University, Guangzhou, China
Duration: 13 Jul 201914 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11691 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Brain Inspired Cognitive Systems
Abbreviated titleBICS 2019
Country/TerritoryChina
CityGuangzhou
Period13/07/1914/07/19

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
  • Computer Science(all)

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