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Applied Sciences
Volume 16
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10.3390/app16010249
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Machine Learning Methods for Predicting Cancer Complications Using Smartphone Sensor Data: A Prospective Study

by
Gabrielė Dargė
1,
Gabrielė Kasputytė
1,
Paulius Savickas
1,
Adomas Bunevičius
2,
Inesa Bunevičienė
3,
Erika Korobeinikova
4,
Domas Vaitiekus
4,
Arturas Inčiūra
4,
Laimonas Jaruševičius
4,
Romas Bunevičius
5,
Ričardas Krikštolaitis
1,
Tomas Krilavičius
1,* and
Elona Juozaitytė
4
1
Faculty of Informatics, Vytautas Magnus University, Studentų 10, Kaunas District, LT-53361 Akademija, Lithuania
2
Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10033, USA
3
Faculty of Political Science and Diplomacy, Vytautas Magnus University, LT-44212 Kaunas, Lithuania
4
Oncology Institute, Lithuanian University of Health Sciences, LT-45434 Kaunas, Lithuania
5
ProIT, LT-09312 Vilnius, Lithuania
*
Author to whom correspondence should be addressed.
Appl. Sci. 2026, 16(1), 249; https://doi.org/10.3390/app16010249 (registering DOI)
Submission received: 19 November 2025 / Revised: 20 December 2025 / Accepted: 23 December 2025 / Published: 25 December 2025
(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare and Precision Medicine, 2nd Edition)
Versions Notes

Abstract

Complications are frequent in cancer patients and contribute to adverse outcomes and higher healthcare costs, underscoring the need for earlier identification and prediction. This study evaluated the feasibility of using passively generated smartphone sensor data to explore early-warning signals of complications and symptom worsening during cancer treatment. A total of 108 patients were continuously monitored using accelerometer, GPS, and screen on/off data collected through the LAIMA application, while symptoms of depression, fatigue, and nausea were assessed every two weeks and complications were confirmed during clinic visits or emergency presentations. Smartphone data streams were aggregated into variables describing activity and sociability patterns. Machine learning models, including Decision Tree, Extreme Gradient Boosting, K-Nearest Neighbors, and Support Vector Machine, were used for complication prediction, and time-series models such as Autoregressive Integrated Moving Average, Holt–Winters, TBATS, Long Short-Term Memory neural network, and General Regression Neural Network were applied to identify early behavioral changes preceding symptom reports. In this exploratory analysis, the ensemble model demonstrated high sensitivity (89%) for identifying complication events. Smartphone-derived behavioral indicators enabled earlier detection of depression, fatigue, and vomiting by about nine days in a subset of patients. These findings demonstrate the feasibility of passive smartphone sensor data as exploratory early-warning signals, warranting validation in larger cohorts.
Keywords: cancer complications; smartphone sensors; digital phenotyping; machine learning cancer complications; smartphone sensors; digital phenotyping; machine learning

Share and Cite

MDPI and ACS Style

Dargė, G.; Kasputytė, G.; Savickas, P.; Bunevičius, A.; Bunevičienė, I.; Korobeinikova, E.; Vaitiekus, D.; Inčiūra, A.; Jaruševičius, L.; Bunevičius, R.; et al. Machine Learning Methods for Predicting Cancer Complications Using Smartphone Sensor Data: A Prospective Study. Appl. Sci. 2026, 16, 249. https://doi.org/10.3390/app16010249

AMA Style

Dargė G, Kasputytė G, Savickas P, Bunevičius A, Bunevičienė I, Korobeinikova E, Vaitiekus D, Inčiūra A, Jaruševičius L, Bunevičius R, et al. Machine Learning Methods for Predicting Cancer Complications Using Smartphone Sensor Data: A Prospective Study. Applied Sciences. 2026; 16(1):249. https://doi.org/10.3390/app16010249

Chicago/Turabian Style

Dargė, Gabrielė, Gabrielė Kasputytė, Paulius Savickas, Adomas Bunevičius, Inesa Bunevičienė, Erika Korobeinikova, Domas Vaitiekus, Arturas Inčiūra, Laimonas Jaruševičius, Romas Bunevičius, and et al. 2026. "Machine Learning Methods for Predicting Cancer Complications Using Smartphone Sensor Data: A Prospective Study" Applied Sciences 16, no. 1: 249. https://doi.org/10.3390/app16010249

APA Style

Dargė, G., Kasputytė, G., Savickas, P., Bunevičius, A., Bunevičienė, I., Korobeinikova, E., Vaitiekus, D., Inčiūra, A., Jaruševičius, L., Bunevičius, R., Krikštolaitis, R., Krilavičius, T., & Juozaitytė, E. (2026). Machine Learning Methods for Predicting Cancer Complications Using Smartphone Sensor Data: A Prospective Study. Applied Sciences, 16(1), 249. https://doi.org/10.3390/app16010249

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