IDEAS home Printed from https://ideas.repec.org/a/aes/dbjour/v6y2016i4p33-40.html
   My bibliography  Save this article

Data mining in healthcare: decision making and precision

Author

Listed:
  • Ionuţ ŢĂRANU

    (University of Economic Studies, Bucharest, Romania)

Abstract

The trend of application of data mining in healthcare today is increased because the health sector is rich with information and data mining has become a necessity. Healthcare organizations generate and collect large volumes of information to a daily basis. Use of information technology enables automation of data mining and knowledge that help bring some interesting patterns which means eliminating manual tasks and easy data extraction directly from electronic records, electronic transfer system that will secure medical records, save lives and reduce the cost of medical services as well as enabling early detection of infectious diseases on the basis of advanced data collection. Data mining can enable healthcare organizations to anticipate trends in the patient's medical condition and behaviour proved by analysis of prospects different and by making connections between seemingly unrelated information. The raw data from healthcare organizations are voluminous and heterogeneous. It needs to be collected and stored in organized form and their integration allows the formation unite medical information system. Data mining in health offers unlimited possibilities for analyzing different data models less visible or hidden to common analysis techniques. These patterns can be used by healthcare practitioners to make forecasts, put diagnoses, and set treatments for patients in healthcare organizations.

Suggested Citation

  • Ionuţ ŢĂRANU, 2016. "Data mining in healthcare: decision making and precision," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 6(4), pages 33-40, May.
    • Handle: RePEc:aes:dbjour:v:6:y:2016:i:4:p:33-40
    as

    Download full text from publisher

    File URL: http://www.dbjournal.ro/archive/22/22_5.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qiang Yang & Xindong Wu, 2006. "10 Challenging Problems In Data Mining Research," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 597-604.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maikel Luis Kolling & Leonardo B. Furstenau & Michele Kremer Sott & Bruna Rabaioli & Pedro Henrique Ulmi & Nicola Luigi Bragazzi & Leonel Pablo Carvalho Tedesco, 2021. "Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development," IJERPH, MDPI, vol. 18(6), pages 1-20, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Hailin, 2017. "Distance measure with improved lower bound for multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 622-637.
    2. DE CNUDDE, Sofie & MARTENS, David & EVGENIOU, Theodoros & PROVOST, Foster, 2017. "A benchmarking study of classification techniques for behavioral data," Working Papers 2017005, University of Antwerp, Faculty of Business and Economics.
    3. Harshita Patel & Dharmendra Singh Rajput & G Thippa Reddy & Celestine Iwendi & Ali Kashif Bashir & Ohyun Jo, 2020. "A review on classification of imbalanced data for wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 16(4), pages 15501477209, April.
    4. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 2018. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 20(2), pages 401-416, April.
    5. Liao, Jui-Jung & Shih, Ching-Hui & Chen, Tai-Feng & Hsu, Ming-Fu, 2014. "An ensemble-based model for two-class imbalanced financial problem," Economic Modelling, Elsevier, vol. 37(C), pages 175-183.
    6. Keng-Hoong Ng & Chin-Kuan Ho & Somnuk Phon-Amnuaisuk, 2012. "A Hybrid Distance Measure for Clustering Expressed Sequence Tags Originating from the Same Gene Family," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-14, October.
    7. Vilém Novák & Soheyla Mirshahi, 2021. "On the Similarity and Dependence of Time Series," Mathematics, MDPI, vol. 9(5), pages 1-14, March.
    8. Riesgo García, María Victoria & Krzemień, Alicja & Manzanedo del Campo, Miguel Ángel & Escanciano García-Miranda, Carmen & Sánchez Lasheras, Fernando, 2018. "Rare earth elements price forecasting by means of transgenic time series developed with ARIMA models," Resources Policy, Elsevier, vol. 59(C), pages 95-102.
    9. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 0. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    10. Hady Suryono & Heri Kuswanto & Nur Iriawan, 2022. "Two-Phase Stratified Random Forest for Paddy Growth Phase Classification: A Case of Imbalanced Data," Sustainability, MDPI, vol. 14(22), pages 1-13, November.
    11. Pancheng Wang & Shasha Li & Haifang Zhou & Jintao Tang & Ting Wang, 2019. "Cited text spans identification with an improved balanced ensemble model," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1111-1145, September.
    12. Yan Li & Manoj Thomas & Kweku-Muata Osei-Bryson & Jason Levy, 2016. "Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management," IJERPH, MDPI, vol. 13(12), pages 1-17, December.
    13. Neda Abdelhamid & Arun Padmavathy & David Peebles & Fadi Thabtah & Daymond Goulder-Horobin, 2020. "Data Imbalance in Autism Pre-Diagnosis Classification Systems: An Experimental Study," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-16, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aes:dbjour:v:6:y:2016:i:4:p:33-40. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Adela Bara (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.