IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i8p1211-d388303.html
   My bibliography  Save this article

Data-Driven Analytics for Personalized Medical Decision Making

Author

Listed:
  • Nataliia Melnykova

    (Artificial Intelligence Department, Institute of Computer Sciences and Information Technologies, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Nataliya Shakhovska

    (Artificial Intelligence Department, Institute of Computer Sciences and Information Technologies, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Michal Gregus

    (Department of Information Systems, Comenius University in Bratislava, 81499 Bratislava, Slovakia)

  • Volodymyr Melnykov

    (General Surgery Department, Danylo Halytsky Lviv National Medical University, 79010 Lviv, Ukraine)

  • Mariana Zakharchuk

    (Linguistically Education Center, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Olena Vovk

    (Artificial Intelligence Department, Institute of Computer Sciences and Information Technologies, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

Abstract

The study was conducted by applying machine learning and data mining methods to treatment personalization. This allows individual patient characteristics to be investigated. The personalization method was built on the clustering method and associative rules. It was suggested to determine the average distance between instances in order to find the optimal performance metrics. The formalization of the medical data preprocessing stage was proposed in order to find personalized solutions based on current standards and pharmaceutical protocols. The patient data model was built using time-dependent and time-independent parameters. Personalized treatment is usually based on the decision tree method. This approach requires significant computation time and cannot be parallelized. Therefore, it was proposed to group people by conditions and to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. The novelty of the paper is the new clustering method, which was built from an ensemble of cluster algorithms, and the usage of the new distance measure with Hopkins metrics, which were 0.13 less than for the k-means method. The Dunn index was 0.03 higher than for the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm. The next stage was the mining of associative rules provided separately for each cluster. This allows a personalized approach to treatment to be created for each patient based on long-term monitoring. The correctness level of the proposed medical decisions is 86%, which was approved by experts.

Suggested Citation

  • Nataliia Melnykova & Nataliya Shakhovska & Michal Gregus & Volodymyr Melnykov & Mariana Zakharchuk & Olena Vovk, 2020. "Data-Driven Analytics for Personalized Medical Decision Making," Mathematics, MDPI, vol. 8(8), pages 1-15, July.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1211-:d:388303
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/8/1211/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/8/8/1211/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Singha, Sumanta & Arha, Himanshu & Kar, Arpan Kumar, 2023. "Healthcare analytics: A techno-functional perspective," Technological Forecasting and Social Change, Elsevier, vol. 197(C).

    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:gam:jmathe:v:8:y:2020:i:8:p:1211-:d:388303. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.