Machine learning cases in clinical and biomedical domains

The aim of this paper is twofold: Firstly, to provide introductory knowledge to the reader who has little or no knowledge of machine learning with examples of applications in clinical and biomedical domains, and secondly, to compare and contrast the concept of Artificial Neural Network (ANN) and the Qur’anic concept of intellect (aql) in the Qur’an. Learning algorithm can generally be categorised into supervised and unsupervised learning. To better understand the machine learning concept, hypothetical data of glaucoma cases are presented. ANN is then selected as an example of supervised learning and the underlying principles in ANN are presented with general audience in mind with an attempt to relate the mechanism employed in the algorithm with Qur’anic verses containing the verbs derived from aql. The applications of machine learning in clinical and biomedical domains are briefly demonstrated based on the author’s own research and most recent examples available from University of California, Irvine Machine Learning Repository. Selected verses which indicate motivation to use the intellect in positive manners and rebuke to those who do not activate the intellect are presented. The evidence found from the verses suggests that ANN shares similar learning process to achieve belief (iman) by analysing the similitudes (amsal) introduced to the algorithm.

[1]  Thorsteinn S. Rögnvaldsson,et al.  State of the art prediction of HIV-1 protease cleavage sites , 2015, Bioinform..

[2]  Masoud Nikravesh,et al.  Feature Extraction - Foundations and Applications , 2006, Feature Extraction.

[3]  Dinesh K. Kumar,et al.  Retinal stroke prediction using logistic-based fusion of multiscale fractal analysis , 2010, 2010 IEEE International Conference on Imaging Systems and Techniques.

[4]  Dong Xu,et al.  Classification of lung cancer using ensemble-based feature selection and machine learning methods. , 2015, Molecular bioSystems.

[5]  Fikret S. Gürgen,et al.  Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings , 2013, IEEE Journal of Biomedical and Health Informatics.

[6]  M. R. Hilmi,et al.  Prediction of Changes in Visual Acuity and Contrast Sensitivity Function by Tissue Redness after Pterygium Surgery , 2017, Current eye research.

[7]  Mohd Zulfaezal Che Azemin,et al.  Supervised Pterygium Fibrovascular Redness Grading Using Generalized Regression Neural Network , 2014, SoMeT.

[8]  Mark Zastrow Google victory at Go stokes AI fear in Korea , 2016 .

[9]  Jennifer Seidl,et al.  The Oxford Dictionary of Current English , 1985 .

[10]  Bálint Antal,et al.  An ensemble-based system for automatic screening of diabetic retinopathy , 2014, Knowl. Based Syst..

[11]  Murtaza Mutahhari,et al.  Understanding the Uniqueness of the Qur'an , 2017 .

[12]  Lili Yulyadi Book Review of Civilizational Transformation and The Muslim World , 1996 .

[13]  Murray Campbell,et al.  Deep Blue , 2002, Artif. Intell..

[14]  E. Brunner,et al.  Revelation and Reason , 1946 .