Optimal Policy Learning for Disease Prevention Using Reinforcement Learning
| Autor: | Marwan Mahmoud, Zhengyong Feng, Muhammad Imtiaz, Syed Atif Ali Shah, Noor Mast, Zahid Alam Khan, M. Irfan Uddin, Mahmoud Ahmad Al-Khasawneh |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Dependency (UML)
Article Subject Computer science Control (management) Population Disease Machine learning computer.software_genre QA76.75-76.765 03 medical and health sciences 0302 clinical medicine Reinforcement learning Computer software 030212 general & internal medicine education 030304 developmental biology 0303 health sciences education.field_of_study business.industry Deep learning Popularity Computer Science Applications Disease prevention Artificial intelligence business computer Software |
| Zdroj: | Scientific Programming, Vol 2020 (2020) |
| ISSN: | 1058-9244 |
| DOI: | 10.1155/2020/7627290 |
| Popis: | Diseases can have a huge impact on the quality of life of the human population. Humans have always been in the quest to find strategies to avoid diseases that are life-threatening or affect the quality of life of humans. Effective use of resources available to human to control different diseases has always been critical. Researchers are recently more interested to find AI-based solutions to control the human population from diseases due to the overwhelming popularity of deep learning. There are many supervised techniques that have always been applied for disease diagnosis. However, the main problem of supervised based solutions is the availability of data, which is not always possible or not always complete. For instance, we do not have enough data that shows the different states of humans and different states of environments, and how all different actions taken by humans or viruses have ultimately resulted in a disease that eventually takes the lives of humans. Therefore, there is a need to find unsupervised based solutions or some techniques that do not have a dependency on the underlying dataset. In this paper, we have explored the reinforcement learning approach. We have tried different reinforcement learning algorithms to research different solutions for the prevention of diseases in the simulation of the human population. We have explored different techniques for controlling the transmission of diseases and its effects on health in the human population simulated in an environment. Our algorithms have found out policies that are best for the human population to protect themselves from the transmission and infection of malaria. The paper concludes that deep learning-based algorithms such as Deep Deterministic Policy Gradient (DDPG) have outperformed traditional algorithms such as Q-Learning or SARSA. |
| Databáze: | OpenAIRE |
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