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A comparison of feature selection methods when using motion sensors data: a case study in Parkinson’s disease
Business School, Örebro University.
Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
Business School, Örebro University.
2018 (Engelska)Ingår i: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, s. 5426-5429Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The objective of this study is to investigate the effects of feature selection methods on the performance of machine learning methods for quantifying motor symptoms of Parkinson's disease (PD) patients. Different feature selection methods including step-wise regression, Lasso regression and Principal Component Analysis (PCA) were applied on 88 spatiotemporal features that were extracted from motion sensors during hand rotation tests. The selected features were then used in support vector machines (SVM), decision trees (DT), linear regression, and random forests models to calculate a so-called treatment-response index (TRIS). The validity, testretest reliability and sensitivity to treatment were assessed for each combination (feature selection method plus machine learning method). There were improvements in correlation coefficients and root mean squared error (RMSE) for all the machine learning methods, except DTs, when using the selected features from step-wise regression inputs. Using step-wise regression and SVM was found to have better sensitivity to treatment and higher correlation to clinical ratings on the Unified PD Rating Scale as compared to the combination of PCA and SVM. When assessing the ability of the machine learning methods to discriminate between tests performed by PD patients and healthy controls the results were mixed. These results suggest that the choice of feature selection methods is crucial when working with data-driven modelling. Based on our findings the step-wise regression can be considered as the method with the best performance.

Ort, förlag, år, upplaga, sidor
2018. s. 5426-5429
Nationell ämneskategori
Teknik och teknologier Annan medicinteknik
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-29426DOI: 10.1109/EMBC.2018.8513683ISBN: 978-1-5386-3646-6 (digital)OAI: oai:DiVA.org:du-29426DiVA, id: diva2:1285936
Konferens
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 18-21 July 2018, Honolulu, HI, USA
Tillgänglig från: 2019-02-05 Skapad: 2019-02-05 Senast uppdaterad: 2019-02-06Bibliografiskt granskad
Ingår i avhandling
1. Automating levodopa dosing schedules for Parkinson’s disease
Öppna denna publikation i ny flik eller fönster >>Automating levodopa dosing schedules for Parkinson’s disease
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Levodopa is mainly used to manage the motor symptoms of PD. However, disease progression and long-term use of levodopa cause reduced medication efficacy and side effects. When that happens, precise individualized dosing schedules are required.

This doctoral thesis in the field of Micro-data analysis introduces an end-to-end solution for the automation of the pharmacological management of PD with levodopa, and offers some insight on levodopa pharmacodynamics. For that purpose, an algorithm that derives objective ratings for the patients’ motor function through wearable sensors is introduced, a method to construct individual patient profiles is developed, and two dosing algorithms for oral and intestinal administration of levodopa are presented. Data from five different sources were used to develop the methods and evaluate the performance of the proposed algorithms.

The dose automation algorithms can work both with clinical and objective ratings (through wearable devices), and their application was evaluated against dosing adjustments from movement disorders experts. Both dosing algorithms showed promise and their dosing suggestions were similar to those of the clinicians.

The objective ratings algorithm had good test-retest reliability and its application during a clinical study was successful. Furthermore, the method of fitting individual patient models was robust and worked well with the objective ratings algorithm. Finally, a study was carried out that showed that about half the patients on levodopa treatment show reduced response during the afternoon hours, pointing to the need for more precise modelling of levodopa pharmacodynamics.

Ort, förlag, år, upplaga, sidor
Borlänge: Dalarna University, 2019
Serie
Dalarna Doctoral Dissertations ; 9
Nyckelord
Parkinson’s disease, levodopa, symptom assessment, symptom management, dosing algorithms, wearable sensors, microtablets, continuous infusion
Nationell ämneskategori
Datavetenskap (datalogi) Datorsystem Systemvetenskap, informationssystem och informatik
Forskningsämne
Komplexa system - mikrodataanalys, FLOAT - Flexibel levodopa-optimerings och individanpassningsteknik
Identifikatorer
urn:nbn:se:du-29435 (URN)978-91-85941-80-3 (ISBN)
Disputation
2019-04-05, sal Clas Ohlson, Borlänge, 13:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2019-03-11 Skapad: 2019-02-06 Senast uppdaterad: 2020-01-10Bibliografiskt granskad

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