Research Papers

Modeling Decision Support System for Optimal Disease Diagnosis and Treatment of Cerebral Aneurysm

[+] Author and Article Information
Kingsley E. Abhulimen

Department of Chemical Engineering,
University of Lagos Nigeria,
Lagos 234, Nigeria;
Syntechsys Corporation, Inc.,
Houston, TX 77077
e-mails: kabhulimen@unilag.edu.ng; syntechsysglobal@gmail.com

Manuscript received April 27, 2018; final manuscript received October 6, 2018; published online January 18, 2019. Assoc. Editor: Carola S. König.

ASME J of Medical Diagnostics 2(2), 021002 (Jan 18, 2019) (26 pages) Paper No: JESMDT-18-1021; doi: 10.1115/1.4041701 History: Received April 27, 2018; Revised October 06, 2018

This paper presents a novel decision support system (DSS) to assist medics administer optimal clinical diagnosis and effective healthcare post-treatment solutions. The DSS model that evolved from the research work predicted treatment of cerebral aneurysm using fuzzy classifications and neural network algorithms specific to patient clinical case data. The Lyapunov stability implemented with Levenberg–Marquardt model was used to advance DSS learning functional paradigms and algorithms in disease diagnosis to mimic specific patient disease conditions and symptoms. Thus, the patients' disease conditions were assigned fuzzy class dummy data to validate the DSS as a functional system in conformity with core sector standards of International Electrotechnical Commission—IEC61508. The disease conditions and symptoms inputted in the DSS simulated synaptic weights assigned linguistic variables defined as likely, unlikely, and very unlikely to represent clinical conditions to specific patient disease states. Furthermore, DSS simulation results correlated with clinical data to predict quantitative coil embolization packing densities required to limit aneurismal inflow, pressure residence time, and flow rate critical to design treatments required. The profiles of blood flow, hazards risks, safety thresholds, and coiling density requirements to reduce aneurismal inflow significantly at lower parent vessel flow rates was predicted by DSS and relates to specific anatomical and physiological parameters for post-treatment of cerebral aneurysm disease.

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Fig. 1

Decision support system training via teacher forcing

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Fig. 2

Decision support system Clinical data register and simulation system

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Fig. 3

Flowchart for DSS neural network tool box computer tomography scan

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Fig. 4

Plot of eigenvalue pressure with model time at artery distance node x = 0.00 for disease factor KD = 1.0

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Fig. 5

Plot of eigenvalue pressure against life time cycle for disease factor KD = 2.0 at location X = 0.0

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Fig. 6

Plot of pressure with time for disease factors KD [1.0, 2.0, 3.0, 5.0]

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Fig. 7

Plot of eigenvalue, pressure for disease factor KD = 5.0 at node X = 0.0

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Fig. 8

matlab DSS neural network training tool

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Fig. 9

Decision support system neural network training performance

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Fig. 10

matlab DSS neural network training state

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Fig. 11

Decision support system neural network fit

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Fig. 12

matlab DSS neural network regression plot

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Fig. 13

Histogram of frequency (counts) with classes (bins)

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Fig. 14

Probability function for fuzzy set of (0) likely to occur

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Fig. 15

MTBF (mean time to fail) of disease system

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Fig. 16

Disease hazard rate for fuzzy class 1 (very likely to occur)

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Fig. 17

(a) Decision support system belief function eij with time (yr) for fuzzy class 1 for 90% treatment level and (b) DSS-belief function eij with time (yr) for fuzzy class 1 for zero 0% treatment level

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Fig. 18

(a) Decision support system belief function eij with time for fuzzy class 2 (90% treatment) and (b) DSS belief variable with time for fuzzy class 2 (1–10) at 0%

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Fig. 19

Decision support system belief function eij with time fuzzy class 3 at failure (1–10) (unlikely to occur 0%)

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Fig. 20

(a) Disease hazard rate with function for fuzzy class 4 at different treatment level and (b) representation of a saccular cerebral aneurysm

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Fig. 21

Plot of disease size with time for a disease factor Kd = 1.0

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Fig. 22

Plot of diseased size with time for diseased factor 3.0

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Fig. 23

Plot of diseased size with time for diseased factor KD = 5.0

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Fig. 24

Pressure variations with time at different axial position kD = 0

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Fig. 25

Pressure variation with time at different axial position KD = 1

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Fig. 26

Pressure variation with time at different axial position KD = 5



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