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Neuroscience patient identification using big data and fuzzy logic–An Alzheimer’s disease case study

Munir, Kamran; de Ramón-Fernández, Alberto; Iqbal, Sohail; Javaid, Nadeem

Authors

Kamran Munir Kamran2.Munir@uwe.ac.uk
Associate Professor in Data Science

Alberto de Ramón-Fernández

Sohail Iqbal

Nadeem Javaid



Abstract

Modern neuroscience imaging technologies considerably affect diagnostic and prognostic accuracy and facilitate progress towards the cure of brain diseases. The benefits largely depend on the practicalities by which the large-scale imaging and clinical data can be integrated, examined and understood. In EU neuGRID4You (N4U) project, many datasets were generated from research centres and hospitals. In order to perform effective analyses, these datasets and their metadata along with a number of pre-computed parameters are stored in a big data repository. This paper focuses on the patient identification using big data and Fuzzy Logic, which has been achieved through fuzzy processing where a reference number called Alzheimer’s Disease Identification Num- ber (ADIN) is calculated. It has enabled patients’ sorting for a particular intensity of Alzheimer’s disease, short-term estimation of the progression of that disease and context of individual patients with respect to other patients such as appropriate treatment, estimated life expectancy etc. The generated rules define the necessary knowledge base for the inference engine to generate output sets and an aggregate membership function of each rule is formed. Using this function, a most representative value of the total output set is obtained which represents the disease intensity. The implemented system and its evaluation are based on realistic datasets, demonstrators and making use of real-life neuroscience case studies. The presented results of four selected case studies show that this approach have provided sufficient expressiveness in understanding patients’ disease information. Finally, a discussion and conclusions are presented on the opportunities offered by the calculation of ADIN to manage Alzheimer’s disease along with potential future extensions or applications of this work.

Journal Article Type Article
Publication Date Dec 1, 2019
Journal Expert Systems with Applications
Print ISSN 0957-4174
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 136
Pages 410-425
APA6 Citation Munir, K., de Ramón-Fernández, A., Iqbal, S., & Javaid, N. (2019). Neuroscience patient identification using big data and fuzzy logic–An Alzheimer’s disease case study. Expert Systems with Applications, 136, 410-425. https://doi.org/10.1016/j.eswa.2019.06.049
DOI https://doi.org/10.1016/j.eswa.2019.06.049
Keywords big data, neuroscience, fuzzy processing, brain disease, analysis, Alzheimer
Publisher URL https://doi.org/10.1016/j.eswa.2019.06.049
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