Disease Classification in Health Care Systems With Game Theory Approach

Bilal Saeed Raja,Sohail Asghar

doi:10.1109/access.2020.2991016

Bilal Saeed Raja, Sohail Asghar

Open Access

https://doi.org/10.1109/access.2020.2991016

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 3	License type: CC BY 4.0

Affiliation: COMSATS University Islamabad

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 co-operative 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.

Highlights

Various knowledge base techniques have been introduced in the medical data mining domain to support accurate, efficient and computationally efficient decision-making for health care improvement [1]
The multi-objective problem is to define an optimized solution for every player taking all strategies as input
This whole process is done interactively for every player and forms a strategy pool known as a solution, which is called NASH EQUILIBRIUM (NE), which depicts that no player can attain additional edge based on their individual strategy by keeping the strategy of other players as unchanged

Summary

Introduction

Various knowledge base techniques have been introduced in the medical data mining domain to support accurate, efficient and computationally efficient decision-making for health care improvement [1]. Keeping in view the evolving nature of multi-objective optimization problems especially in medical sciences, one of the recent solutions to solving such problems is the evolutionary algorithms. Evolutionary algorithms base their outcome on Pareto Solution set, which has been further defined in the subsequent section of this paper. EA may not be able to devise a solution by optimizing all conflicting parameters instead it will offer the optimized solution based on those conflicting objectives. In the case of multi-objective problems, the resultant outcome should satisfy all objectives at the same time. In such cases, there is usually more than one solution that satisfies each objective, which forms Pareto Optimal Solution

Objectives

Findings

Discussion

Conclusion