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Authors: Yomna Omar ; Abdullah Tasleem ; Michel Pasquier and Assim Sagahyroon

Affiliation: American University of Sharjah, United Arab Emirates

Keyword(s): Intelligent Healthcare Tool, Lung Cancer Prognosis, Real-World Data Mining, WEKA.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Cardiovascular Technologies ; Computing and Telecommunications in Cardiology ; Data Engineering ; Data Management and Quality ; Data Manipulation ; Data Mining ; Data Visualization ; Databases and Information Systems Integration ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Health Information Systems ; Knowledge-Based Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Software Systems in Medicine ; Symbolic Systems

Abstract: This paper describes a Lung Cancer Prognosis System (LCPS) that aims at providing oncologists with an accurate estimate of the health status of their patients. The proposed system is born from two observations: First, lots of efforts are still required in healthcare to improve productivity, accuracy, etc. by providing ad hoc computer-based solutions; second, while increasing popular, AI and data mining tools cannot be used without significant training and expertise. LCPS thus aims at providing the former by integrating the latter into a user-friendly tool, supplementing the knowledge of the expert oncologist with information about their patients, and leading to improved patient care and treatments. LCPS can accept a variety of lung cancer datasets and employs several data mining algorithms to uncover relationships between observed health signs and probable outcomes, and provides oncologists with various statistical results including predictions about their patients’ medical future. F urthermore, LCPS makes it easy to manage patients’ records, allows them view their profiles and any information as deemed suitable by their doctor, including prognosis and other comments. Lastly, while the current application is currently limited to lung cancer treatment, it can be considered a prototype that can be adapted to other diseases. (More)

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Paper citation in several formats:
Omar, Y.; Tasleem, A.; Pasquier, M. and Sagahyroon, A. (2018). Lung Cancer Prognosis System using Data Mining Techniques. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - HEALTHINF; ISBN 978-989-758-281-3; ISSN 2184-4305, SciTePress, pages 361-368. DOI: 10.5220/0006553703610368

@conference{healthinf18,
author={Yomna Omar. and Abdullah Tasleem. and Michel Pasquier. and Assim Sagahyroon.},
title={Lung Cancer Prognosis System using Data Mining Techniques},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - HEALTHINF},
year={2018},
pages={361-368},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006553703610368},
isbn={978-989-758-281-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - HEALTHINF
TI - Lung Cancer Prognosis System using Data Mining Techniques
SN - 978-989-758-281-3
IS - 2184-4305
AU - Omar, Y.
AU - Tasleem, A.
AU - Pasquier, M.
AU - Sagahyroon, A.
PY - 2018
SP - 361
EP - 368
DO - 10.5220/0006553703610368
PB - SciTePress