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A Machine Learning-Based Statistical Analysis of Predictors for Spinal Cord Stimulation Success
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Spinal Cord Stimulation (SCS) is a treatment for lumbar back pain and despitethe proven effcacy of the technology, there is a lack of knowledge in how the treatment outcome varies between different patients groups. Furthermore, since the method is costly, in the sense of material, surgery and follow-up time, a more accurate patient targeting would decrease healthcare costs.

Within recent years, Real World Data (RWD) has become a vital source of information to describe the effects of medical treatments. The complexity, however, calls for new, innovative methods using a larger set of useful features to explain the outcome of SCS treatments. This study has employed machine learning algorithms, e.g., Random Forest Classier (RFC) boosting algorithms to finally compare the result with the baseline; Logistic regression (LR).

The results retrieved was that RFC tend to classify successful and unsuccessful patients better while logistic regression was unstable regarding unbalanced data. In order to interpret the insights of the models, we also proposed a Soft Accuracy Measurement (SAM) method to explain how RFC and LR differ.

Some factors have shown to impact the success of SCS. These factors were age, income, pain experience time and educational level. Many of these variables could also be supported by earlier studies on factors of success from lumbar spine surgery.

Place, publisher, year, edition, pages
2019. , p. 66
Keywords [en]
Machine Learning, Predictors, Spinal Cord Stimulation, SCS, Outcomes
National Category
Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-162121OAI: oai:DiVA.org:umu-162121DiVA, id: diva2:1342867
External cooperation
Quantify Research
Educational program
Master of Science in Engineering and Management
Presentation
2019-06-05, Umeå, 15:21 (English)
Supervisors
Examiners
Available from: 2019-08-15 Created: 2019-08-14 Last updated: 2019-08-15Bibliographically approved

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