Machine learning for classifying the early stage of Osteoarthritis based on biological data
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Åberg, Lina
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Osteoarthritis, or OA, is a chronic joint disease and the most common form of arthritis.
It is a very common disease in human athletes, but also the most common reason
for lameness and poor performance in animal athletes, such as racehorses. The traditional
standard for diagnosing OA is by radiographic measurements. Unfortunately,
clinically recognizable changes do not appear until the chronic destruction of the
articular cartilage has progressed too far and the disease is irreversible.
In order to diagnose the disease earlier, the focus has been shifted from imaging
biomarkers to biological biomarkers. Several promising biological biomarkers have
been found by researchers at SLU and Sahlgrenska, each representing a different
stage of the destruction process. One specific biomarker has shown to increase in
both blood and synovial fluid in horses with acute lameness, corresponding to an
early stage of OA. If this early OA could be identified, it would be possible to intervene
in time and the chronic and painful destruction of the joint tissues could be
prevented, which could greatly improve the equine welfare.
The aim of this thesis was to investigate different machine learning approaches in
order to find a promising method to be used in a decision support system for practitioners.
The future system should be able to help diagnose OA, and specifically
identify the different progression stages of structural changes in the joint, based on
biological data. A Random Forest Classifier was developed along with a Spectral
Clustering Algorithm, which was trained and evaluated on datasets with samples
from both synovial fluid and serum. The results indicate some promise for the future
decision support system, which will have to be evaluated further once more data is
collected and the biomarkers for the remaining progression stages are added in the
mix.
Beskrivning
Ämne/nyckelord
machine learning , engineering , biomarkers , decision support system , random forest , spectral clustering