Skip navigation
Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01pz50gz849
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorJha, Niraj K-
dc.contributor.authorDeokule, Tanvee-
dc.date.accessioned2018-08-20T15:03:47Z-
dc.date.available2018-08-20T15:03:47Z-
dc.date.created2018-05-07-
dc.date.issued2018-08-20-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01pz50gz849-
dc.description.abstractThe menstrual cycle is a crucial indicator of a woman's overall health. Accurate tracking and forecasting will lead to maintenance of general fitness and early diagnosis of chronic, hormone-dependent disorders of the female reproductive system. In this paper we develop a robust, personalized model that can infer hormone dynamics and predict the onset of menstruation both for healthy individuals and for those with disorders of the reproductive system. We take advantage of physiological signals from minimally invasive wearable medical sensors, specifically, acceleration, electrodermal activity (EDA), galvanic skin response, blood volume pulse and blood pressure for continuous data collection. A thorough data analysis procedure comprising pre-processing, feature extraction and feature selection (forward attribute and Principal Component Analysis) is implemented. The comprehensive set of selected features is fed into base regression algorithms, followed by stacked regression and an ensemble of autoregressive integrated moving average (ARIMA) predictions. The ensemble stacked model trained on the ARIMA integrated feature set accurately predicts the onset of menstruation with an overall root mean square error (RMSE) of 1.17 days. The prediction accuracy goes on increasing as the onset date approaches, with a RMSE of 1.01 days three weeks in advance, 0.21 days with two weeks to go and 0.04 days during the week of menstruation.en_US
dc.format.mimetypeapplication/pdf-
dc.language.isoenen_US
dc.titleMachine Learning Approaches for Menstrual Cycle Trackingen_US
dc.typePrinceton University Senior Theses-
pu.date.classyear2018en_US
pu.departmentElectrical Engineeringen_US
pu.pdf.coverpageSeniorThesisCoverPage-
pu.contributor.authorid961074113-
pu.certificateApplications of Computing Programen_US
Appears in Collections:Electrical Engineering, 1932-2020

Files in This Item:
File Description SizeFormat 
DEOKULE-TANVEE-THESIS.pdf7.76 MBAdobe PDF    Request a copy


Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.