A major healthcare problem is the overcrowding of hospitals and emergency departments which leads to negative patient outcomes and increased costs. In a previous study, performed by Leiden University Medical Centre, a new and innovative prehospital triage method was developed where two nurse paramedics could consult a cardiologist for patients with cardiac symptoms, via a live connection on a digital triage platform. The developed triage method resulted in a recall = 0.995 and specificity = 0.0113. This study arise the following research question: ‘Would there be enough (good) information gathered on the prehospital scene to make a machine learning model able to predict myocardial infarction?’. By testing different pre-processing steps, several features (premade ones and self-made ones), multiple models (Support Vector Machine, K Nearest Neighbour, Logistic Regression and Random Forest), various outcome settings and hyperparameters, led to the final results: recall = 0.995 and specificity = 0.1101. This is gained through the feature selected by a cardiologist and the Support Vector Machine model. The outcomes are controlled by an extra explainability layer named Explain Like I’m Five. This outcome illustrates that the created machine learning model is trained mostly on the right words and characters.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-84925 |
| Date | January 2021 |
| Creators | Van der Haas, Yvette Jane |
| Publisher | Luleå tekniska universitet, Datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0021 seconds