Implementering av maskinginlärningsmodeller för detektering av ett objekt baserad på endimensionell elektromagnetisk strålningsdata

Sammanfattning: Clinical trials are experiments or observations on a patient’s responses of different medical treatments to cure diseases. Such trials are heavily regulated and must achieve a certain quality standard of the trial and clinical adherence is a determining factor on the success of a study. However, it has historically been difficult to systematically follow and understand patient adherence to medical ordinations, predominately due to lack of proper tools. One new type of tools is a digital pillbox that can be used to supply pills to participants in clinical trials. This paper examines implementing two supervised machine learning models to detect if an object (a pill) is found in an encapsulated compartment (pillbox) based on electromagnetic radiation data from a proximity sensor. Support Vector Machine (SVM) and Random Forest (RF) were evaluated on a data set of N=1,485 observations, consisting of five classes: four different pills and ‘no pill’. RF performs best with accuracy of 98.0% and weighted average precision of 98.0%. SVM had 97.3% accuracy and 97.6% weighted average precision. Best performance was achieved at N=1,000 for RF and 1,100 for SVM. The conclusion was that a high accuracy and precision can be achieved using either RF or SVM. The classification model strengthens the value proposition of a digital pillbox and can improve clinical trials to achieve better data quality. However, for the model to contribute actual economical value, digital pillboxes must be a common practice in clinical trials.