Disease Classification in Health Care Systems With Game Theory Approach

Abstract

There are numerous cases in real life when we come across problems involving the optimization of multiple objectives simultaneously. One of the complexities of solving such problems is that often one or more objectives are usually conflicting under given conditions. In this study, the benefits of relying on a deployed Clinical Decision Support System (CDSS) concerning the overall reputation of a health facility has been studied. The analysis is performed in terms of a cooperative Bayesian game-theoretic model. The game is played between two players of which the first player is a patient who needs quick and accurate medical attention and the second player is the hospital administration that relies on medical experts as well as integrated multi-objective clinical data classification systems for decision-making. The proposed model ''MEAF''-Multi-objective Evolutionary Algorithm using Fuzzy Genetics attempts to address accuracy and interpretability simultaneously using Evolutionary Algorithms (EAs). This model enables a H CDSS to detect a disease accurately by using the available resources efficiently. The results of our simulation show that H CDSS produces better and accurate results in detecting disease with efficient resource utilization along with the reduced computational cost. This approach has also produced a better response for both players based on Bayesian Nash Equilibrium. Finally, the proposed model has been tested for accuracy, efficient resource utilization, and computationally cost-effective solution.

Optimizing Patient Recruitment for Clinical Trials: A Hybrid Classification Model and Game-Theoretic Approach for Strategic Interaction

“…Game theory also offers a framework for decision-making in clinical settings, such as clinical decision support systems, by considering the interactions between patients and healthcare providers [13]. Raja et al [14] proposed one such framework, which uses a Bayesian game-theoretic approach to optimize disease classification in healthcare systems. Game theoretic frameworks have worked around the ideal care of patients as well.…”

A Survey of Cyber-Physical Systems From a Game-Theoretic Perspective

“…A cooperative game-based leukemia classification approach is proposed in [373] using a data set containing 400 samples of human leukemic bone marrow. A cooperative Bayesian game model is proposed in [374] to analyze the benefit of relying on a clinical decision support system for the overall reputation of a health facility. In [375], the authors model cancer treatment as a game and use an evolutionary game theoretic approach to decide the optimal amount of drug for chemotherapy.…”

Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review

“…Decision trees for DB-CDSS [22,30] represent in forms of graph structure and provide clinical interpretation of traversal rules in nodes of the tree to make decisions. Te Bayesian algorithm for DB-CDSS [49] is based on prior probability for prediction. Each of these models has its advantages and disadvantages: logistic regression has a simple structure and strong interpretability for linear data and small datasets; decision trees have a transparent structure, and they can implement large-scale data sources in a relatively short time, and the Bayesian model has the advantage of stable classifcation efciency for a large scale of data with fewer features.…”

“…Most research focuses on issues of the black box from a technological perspective, with limited attention given to the need for interpretability from a medical perspective. In reviewing the literature, the needs of clinicians include eight categories: (1) visualization representation of a process or clinical variable proxies for clinician decision-making [2,7,22,29,30,51,52,54], (2) accessibility and reliability of patients' data [4,17,19,[49][50][51]55], (3) interface of doctors-patients or human-computer interaction for interpreting outcomes [4,22,50], (4) transparent structure for users to validate outputs of the model with domain knowledge [2,7,20], (5) identifcation of biomarkers for supporting decision-making [3,29,51], (6) feature selection distilling information overload [9,19,20,52], (7) rule of representation for knowledge [2,19,20,53], and (8) clinicians' needs incorporated into the clinical workfow [7]. Te needs of patients for interpretability include (1) collecting patients' data of symptoms, physical exams, treatment, and reports of procedures and laboratory tests [4,17,19,50], (2) interface of doctors-patients interaction for interpreting outcomes [50], (...…”

“…Te DB-CDSS has the so-called white and black box models. Te white box methods typically use logistic regression (LG) [4,7,29], decision trees [22,30], and the Bayesian [49]. Te logistic regression model provides clinical insights of feature importance, but it performs poorly for nonlinear datasets.…”