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https://github.com/daffidwilde/thesis

A repository to accompany my PhD thesis
https://github.com/daffidwilde/thesis

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A repository to accompany my PhD thesis

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README 

        
# New methods for algorithm evaluation and cluster initialisation with applications to healthcare



A repository for my PhD thesis at Cardiff University with the Cwm Taf Morgannwg

University Health Board (UHB).



## Cloning the repo



To clone the repository locally run the following command:



```

$ git clone --recurse-submodules https://github.com/daffidwilde/thesis.git

```



Note that command includes the option `--recurse-submodules`. This is because

there are several submodules to this repository.



## Environment and requirements



All the code for this thesis is written in Python, with all necessary versions

specified in `environment.yml`. This file configures an

[Anaconda virtual environment][envs].



With Anaconda installed, run the following commands to create and activate the

environment:



```

$ conda env create -f environment.yml

$ source activate thesis

```



## Compiling the thesis



This document has been prepared using LaTeX and can be compiled as intended

with any tool that offers a shell escape flag. Examples include `latexmk` and

`pdflatex`.



However, a number of `invoke` tasks have been [written](tasks.py) to ease the

compilation and testing of this thesis. So, the easiest way to compile the

document is to activate the `thesis` environment and run the following command:



```

$ inv compile

```



## Abstract



This thesis explores three themes related to modern operational research:

evaluating the objective performance of an algorithm, combining clustering with

concepts of mathematical fairness, and developing insightful healthcare models

despite a lack of fine-grained data.



The established evaluation procedure for algorithms --- and particularly machine

learning algorithms --- lacks robustness, potentially inflating the success of

the methods being assessed. To tackle this, the evolutionary dataset

optimisation method is introduced as a supplementary evaluation tool. By

traversing the space in which datasets exist, this method provides the means of

attaining a richer understanding of the algorithm under study.



This method is used to investigate a novel initialisation method for a

centroid-based clustering algorithm, k-modes. The initialisation makes use of a

matching game to allocate the starting centroids in a mathematically fair way.

The subsequent investigation reveals the conditions under which the new

initialisation improves upon two other initialisation methods.



An extension to the k-modes algorithm is utilised to segment an administrative

dataset provided by the co-sponsors of this project, the Cwm Taf Morgannwg UHB.

The dataset corresponds to the patient population presenting a specific chronic

disease, and comprises a high-level summary of their stays in hospital over a

number of years. Despite the relative coarseness of this dataset, the

segmentation provides a useful profiling of its instances. These profiles are

used to inform a multi-class queuing model representing a hypothetical ward for

the affected patients. Following a novel validation process for the queuing

model, actionable insights into the needs of the population are found.



In addition to the research reported in this thesis, several open-source

software packages have been developed to accompany this thesis. These pieces of

software were developed using best practices to ensure the reliability,

reproducibility, and sustainability of the research in this thesis.



## Additional software



Directions for the relevant software packages to accompany this thesis are

listed in the table below.



| Name       | Repository             | Documentation                         |

|------------|------------------------|---------------------------------------|

| `edo`      | [daffidwilde/edo]      | [edo.readthedocs.io][edo-docs]        |

| `matching` | [daffidwilde/matching] | [matching.readthedocs.io][match-docs] |

| `edolab`   | [daffidwilde/edolab]   | [Repository `README`][edolab-readme]  |



[envs]: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html

[daffidwilde/edo]: https://github.com/daffidwilde/edo

[edo-docs]: https://edo.readthedocs.io

[daffidwilde/matching]: https://github.com/daffidwilde/matching

[match-docs]: https://matching.readthedocs.io

[daffidwilde/edolab]: https://github.com/daffidwilde/edolab

[edolab-readme]: https://github.com/daffidwilde/edolab/blob/main/README.md