Genetic Algorithm based Optimized Radiotherapy Patient Scheduling
| dc.contributor.advisor | Dawit, Assefa | |
| dc.contributor.author | Metadel, Hailu | |
| dc.date.accessioned | 2022-06-28T05:55:06Z | |
| dc.date.accessioned | 2023-11-04T15:22:12Z | |
| dc.date.available | 2022-06-28T05:55:06Z | |
| dc.date.available | 2023-11-04T15:22:12Z | |
| dc.date.issued | 2022-02 | |
| dc.description.abstract | Radiotherapy is the major means to treat cancer patients. Radiotherapy comprises two phases: pretreatment and treatment on radiation machines. This thesis work focuses on the treatment phase. Treatment consists of multiple, almost daily irradiation appointments, followed by optional imaging and control assignments. The scheduling of radiotherapy appointments is a complex problem due to various medical and scheduling constraints, such as patient category, machine availability, waiting time targets and also due to the size of the problem (i.e., number of machines, facilities and patients). The objective of this thesis is to minimize waiting time and maximize device utilization in) patient’s appointment scheduling. Thus, this thesis presents an optimization algorithm for scheduling of radiotherapy treatments for categorized cancer patients. In order to manage patient information effectively in digital data format a web application is built. This web application registers users (professionals) that are responsible to register patients and includes a database to store patient’s information. Following this, custom genetic algorithm (GA) is developed considering constraints primarily patient category and the rest constraints such as patient in date and time, number of fractions, number of machine and also working days and working hour. Moreover, for the GA to be user friendly a desktop application with graphical user interface (GUI) is developed. The GUI supports the medical professionals to easily manipulate the GA parameters such as number of populations, crossover probability, and mutation probability and also change the dynamic resources or attributes like number of machines, number of patients treated per single machine and number of working days. As a result, the medical professional can schedule patients dynamically. In this thesis best GA performances (i.e., fitness value of 88% - 96.67% accuracy) are obtained for probability crossover (Pc) values between 60% - 80% and probability of mutation (Pm) between 20% - 40%. This means if the health professional sets the cross-over and mutation probability in these ranges, the scheduling will have better optimization, i.e. prioritize high-risk patients, minimize high risk patient waiting time, thus better care for patients. From the resuls, emergency patients are able to get early treatment than radical patients. Compared to traditional manual scheduling, where scheduling is done based on patients arrival date, GA based scheduling enables to prioritize higher risk patients. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/32157 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Radiotherapy scheduling | en_US |
| dc.subject | genetic algorithm | en_US |
| dc.subject | optimize patient schedule | en_US |
| dc.title | Genetic Algorithm based Optimized Radiotherapy Patient Scheduling | en_US |
| dc.type | Thesis | en_US |