Machine Learning for Handoffs Classification Based on Effective Communication History

Main Article Content

Anita Ira Agustina Simbolon Maria Pujiastuti Indra Kelana Jaya Kerista Tarigan Marzuki Sinambela


An important step in data effective communication in handoffs process analysis is data exploration and representation. Communication in handoff treatment is crucial to protect the patients and it can lead to patient’s safety, discontinue care of a patient or the cause loss of important information related to the continuum of care. In this case, we use the machine learning technique by using Support Vector Machine for classification the handoffs for twenty weeks to analysis and represented based on the effective communication history. We used handoffs dataset which employed from Arifin Achmad Hospital in Pekanbaru, Indonesia. The result indicated the performance of the designed system was successful and could be used in handoffs analysis based on the effective communication histories in Arifin Achmad Hospital in Pekanbaru, Indonesia.


Download data is not yet available.

Article Details

How to Cite
SIMBOLON, Anita Ira Agustina et al. Machine Learning for Handoffs Classification Based on Effective Communication History. SinkrOn, [S.l.], v. 3, n. 2, p. 265-267, mar. 2019. ISSN 2541-2019. Available at: <>. Date accessed: 23 may 2019.
**************** Abstract viewed = 32 times ****************


Artamonov, Y. S. (2017). Prediction of Cluster System Load Using Artificial Neural Networks, (1), 59–63.
Beaumont, K., & Russell, J. (2012). Standardizing for reliability: the contribution of tools and checklists. Nursing Standard (Royal College of Nursing (Great Britain) : 1987), 26(34), 35–39.

Chaboyer, W., McMurray, A., Wallis, M., & Chang, A. H. (2008). Standard operating protocol for implementing bedside handover in nursing. Research Centre for Clinical and Community Practice Innovation.

Mahdavi, N. (2013). Short term load forecasting using Bayesian neural networks, 3(2), 1–9.

Mandal, I. (2012). SVM-PSO based Feature Selection for Improving Medical D diagnosis R reliability using Machine Learning Ensembles, 267–276.

Meier, T. B., Deshpande, A. S., Vergun, S., Nair, V. A., Song, J., Biswal, B. B., … Prabhakaran, V. (2012). Support vector machine classification and characterization of age-related reorganization of functional brain networks. NeuroImage, 60(1), 601–613.

Nikaya, N., Abed-saeedi, Z., Azargashb, E., & Alavi-majd, H. (2014). Problems of clinical nurse performance appraisal system: A qualitative study. Asian Nursing Research, 8(1), 15–22.

Sasirekha, A., & Kumar, P. G. (2013). Support Vector Machine For Classification of Heartbeat Time Series Data, (10), 38–41.