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Height Prediction for Growth Hormone Deficiency Treatment Planning Using Deep Learning

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

Authors

Muhammad Ilyas

Jan Sher Khan

Ahsen Tahir

Ahsan Adeel

Hadi Larijani

M. Guftar Shaikh



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.

Presentation Conference Type Conference Paper (Published)
Conference Name 10th International Conference, BICS 2019
Start Date Jul 13, 2019
End Date Jul 14, 2019
Online Publication Date Feb 1, 2020
Publication Date 2020
Deposit Date Apr 21, 2020
Publisher Springer
Pages 76-85
Series Title Lecture Notes in Computer Science
Series Number 11691
Series ISSN 0302-9743
Book Title Advances in Brain Inspired Cognitive Systems
ISBN 9783030394301
DOI https://doi.org/10.1007/978-3-030-39431-8_8
Keywords Growth Hormone Deficiency, Deep learning, Levenberg Marquardt (LM) learning, Root Mean Square, Normalized Root Mean Square, Height prediction
Public URL http://researchrepository.napier.ac.uk/Output/2654540