An Interrelated Decision-Making Model for an Intelligent Decision Support System in Healthcare

被引:6
|
作者
Mahiddin, Normadiah [1 ]
Othman, Zulaiha Ali [1 ]
Abu Bakar, Azuraliza [1 ]
Rahim, Nur Arzuar Abdul [2 ]
机构
[1] Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Ctr Artificial Intelligence Technol CAIT, Bangi 43600, Malaysia
[2] Univ Sains Malaysia, Dept Clin Med, Adv Med & Dent Inst, Kepala Batas 13200, Penang, Malaysia
关键词
Medical services; Decision support systems; Decision making; Spread spectrum communication; Diseases; Information management; Data mining; Healthcare; diabetes; data mining; decision support system; decision-making; DIAGNOSIS; CLASSIFICATION; ARCHITECTURE; PREDICTION; ANALYTICS; PAIN; B40;
D O I
10.1109/ACCESS.2022.3160725
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The nature of decision making in healthcare is complex and crucial. It is essential to have a tool that helps with accurate and correct decisions based on real-time data. Moreover, the healthcare process itself is complex, comprising various stages from primary to palliative, closely related to each other, and the process is different depending on the type of disease. Each stage has a crucial decision to be made relying on other stages' decisions. Thus, an intelligent decision support system (IDSS) model based on a data mining approach becomes a prominent solution. However, the existing IDSS and Group Decision Support System (GDSS) applied a single-stage approach and primarily focused on development at a certain stage for specific outcomes. In contrast, the nature of healthcare decision-making in each stage is related to the previous stages, which change dynamically. Therefore, this paper proposes an interrelated decision-making model (IDM) for IDSS in healthcare that aims to have an effective decision by utilizing knowledge from previous and following treatment stages known as IDM-IDSS-healthcare. The experiment was conducted using simulated diabetes treatments data that were validated by the medical expert. Eight data sets with distinct sizes were constructed and classified into two types of decision-making categories. Each data sets consists of primary and secondary care stages with a range of 25 to 58 attributes and 300-11,000 instances. The experiment results show algorithms J48, Logistic, NaiveBayes Updateable, RandomTree, BayesNet and AdaBoostM1 obtained the best accuracy in sequence from 46% to 99%. The result also shows the improvement of decision-making efficiency with the prediction model accuracy has increased up to 56%. In addition, all respondents agreed in a focus-group discussion with medical and information technology (IT) experts that the proposed IDM-IDSS-healthcare is practical as a healthcare solution. Moreover, the solution for the development of IDM-IDSS-healthcare should use the multi-agent approach.
引用
收藏
页码:31660 / 31676
页数:17
相关论文
共 50 条
  • [1] The Role of Big Data Analytics in Revolutionizing Diabetes Management and Healthcare Decision-Making
    Nauman, Muhammad
    Almadhor, Ahmad S.
    Albekairi, Mohammed
    Ansari, Ali R.
    Fayyaz, Muhammad A. B.
    Nawaz, Raheel
    IEEE ACCESS, 2025, 13 : 10767 - 10785
  • [2] Advances in Intelligent Decision-Making Technology Support
    Tweedale, Jeffrey W.
    Phillips-Wren, Gloria
    Jain, Lakhmi C.
    INTELLIGENT DECISION TECHNOLOGY SUPPORT IN PRACTICE, 2016, 42 : 1 - 15
  • [3] DDM - DECISION-SUPPORT SYSTEM FOR HIERARCHICAL DYNAMIC DECISION-MAKING
    BADIRU, AB
    PULAT, PS
    KANG, M
    DECISION SUPPORT SYSTEMS, 1993, 10 (01) : 1 - 18
  • [4] Influence of environmental factors on human-like decision-making for intelligent ship
    Xue, Jie
    Chen, Zhijun
    Papadimitriou, Eleonora
    Wu, Chaozhong
    Van Gelder, P. H. A. J. M.
    OCEAN ENGINEERING, 2019, 186
  • [5] Tax Intelligent Decision-Making Language Model
    Zhong, Yan
    Wong, Dennis
    Lan, Kun
    IEEE ACCESS, 2024, 12 : 146202 - 146212
  • [6] Development of intelligent system to support management decision-making in education
    Uvalieva, Indira
    Garifullina, Zhadyra
    Utegenova, Anar
    Toibayeva, Shara
    Issin, Bakhtiyar
    2015 6TH INTERNATIONAL CONFERENCE ON MODELING, SIMULATION, AND APPLIED OPTIMIZATION (ICMSAO), 2015,
  • [7] Tactical Decision Support System to Aid Commanders in Their Decision-Making
    Stodola, Petr
    Mazal, Jan
    MODELLING AND SIMULATION FOR AUTONOMOUS SYSTEMS, MESAS 2016, 2016, 9991 : 396 - 406
  • [8] THE CONTROLLING MODEL AS MANAGEMENT SUPPORT IN DECISION-MAKING
    Bolfek, Berislav
    EKONOMSKI VJESNIK, 2010, 23 (01): : 94 - 113
  • [9] A study of CBR and decision-making support system
    Li, Guangyuan
    PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON RISK ANALYSIS AND CRISIS RESPONSE, 2007, 2 : 106 - 109
  • [10] The Decision-Making Model is Determined by the Decision-Making Cost
    Yong, Tan
    SOCIAL SCIENCE AND EDUCATION, 2013, 9 : 195 - 198