Medical data classification with artificial immune system
Opiekun pracy: dr hab. inż. Robert Czabański, prof. PŚ
Clinical decision support systems (CDSS) are gaining more and more attention in healthcare today. Their aim is to assist clinicians in their complex decision-making processes. Many systems of this type are defined as binary classifiers providing assessments of the patient's condition (normal/abnormal) based on the data which includes symptoms, their intensity and results of specific diagnostic tests. Such classification tasks can be successfully solved with a help of computational intelligence methods, and in particular, by using artificial immune systems (AIS) constitute a class of biologically-inspired methods, machine learning algorithms based on discovered functions and behavior of human (mammalian) immune system.
The aim of the work is to design, develop and test a diagnostic decision support system based on an artificial immune system. The effectiveness of the solution will be verified on the basis of benchmark medical data.
