This critical synopsis of prior work by Luca Parisi is submitted in support of a
PhD by Published Work. The work focuses on deriving accurate, reliable and
explainable clinical decision support systems as minimum clinically viable
solutions leveraging Machine Learning (ML) and evolutionary algorithms, for
the first time, to facilitate early diagnostic predictions of Parkinson's Disease
and hypothermia in hospitals, as well as prognostic predictions of optimal
postoperative recovery area and of chronic hepatitis. Despite the various
pathological aetiologies, the underlying capability of ML-based algorithms to
serve as a minimum clinically viable solution for predicting early diagnosis and
prognosis has been thoroughly demonstrated. Feature selection (FS) is a
proven method for increasing the performance of ML-based classifiers for
several applications. Although advances in ML, such as Deep Learning (DL),
have denied the usefulness of any extrinsic FS by incorporating it in their
architectures, e.g., convolutional filters in convolutional neural networks, DL algorithms often lack the required explainability to be understood and
interpreted by clinicians within the context of the diagnostic and prognostic
tasks of interest. Their relatively complicated architectures, the hardware
required for running them and the limited explainability or interpretability of
their architectures, the decision-making process – although as assistive tools
- driven by the algorithms’ training and predictive outcomes have hindered
their application in a clinical setting. Luca Parisi’s work fills this translational
research gap by harnessing the explainability of using traditional ML- and
evolutionary algorithms-based FS methods for improving the performance of
ML-based algorithms and devise minimum viable solutions for diagnostic and
prognostic purposes. The work submitted here involves independent research work, including collaborative studies with Marianne Lyne Manaog
(MedIntellego®) and Narrendar RaviChandran (University of Auckland). In
particular, conciliating his work as a Senior Artificial Intelligence Engineer and
volunteering commitment as the President and Research Committee Leader
of a student-led association named the “University of Auckland Rehabilitative
Technologies Association”, Luca Parisi decided to embark on most research
works included in this synopsis to add value to society via accurate, reliable
and explainable, hence clinically viable applications of AI. The key findings of
these studies are: (i) ML-based FS algorithms are sufficient for devising
accurate, reliable and explainable ML-based classifiers for aiding prediction of early diagnosis for Parkinson’s Disease and chronic hepatitis; (ii) evolutionary
algorithms-based optimisation is a preferred method for improving the
accuracy and reliability of decision support systems aimed at aiding early
diagnosis of hypothermia; (iii) evolutionary algorithms-based optimisation methods enable to devise optimised ML-based classifiers for improving
postoperative discharge; (iv) whilst ML-based algorithms coupled with ML based FS methods are the minimum clinically viable solution for binary
classification problems, ML-based classifiers leveraging evolutionary
algorithms for FS yield more accurate and reliable predictions, as reducing the
search space and overlapping regions for tackling multi-class classification
problems more effectively, which involve a higher number of degrees of
freedom. Collectively, these findings suggest that, despite advances in ML,
state-of-the-art ML algorithms, coupled with ML-based or evolutionary
algorithms for FS, are enough to devise accurate, reliable and explainable
decision support systems for performing both an early diagnosis and a prediction of prognosis of various pathologies.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/18417 |
| Date | January 2019 |
| Creators | Parisi, Luca |
| Contributors | Neagu, Daniel, Campean, Felician |
| Publisher | University of Bradford, Faculty of Engineering and Informatics |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, PhD |
| Rights | <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. |
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