Background: Computer-assisted diagnosis (CAD) has become a common practice of use in healthcare industry due to its improved accuracy and reliability. The CAD systems are expected to improve the quality of medical care by assisting healthcare professionals with wide range of clinical decisions. A CAD system is a combination of computer-assisted detection (CADe) and computer assisted diagnosis (CADx) system.
Objective: The objective of this research article is to generate an optimized rule-set for medical diagnosis capable of providing improved accuracy. It is evident from the literature that keeping a balance between these performance parameters is a real challenge.
Method: In order to achieve the desired objective, the following two contributions has been proposed to improve diagnosis accuracy: 1) an unsupervised feature selection approach based on ACO Meta-heuristic is used to design the CADe system, and 2) an ACO assisted decision tree classifier technique is employed to make CADx system.
Result and Discussion: Three popular UCI (Wisconsin Breast Cancer, Pima Indian Diabetes and Liver Disorder) medical domain datasets have been used to evaluate the performance of proposed model. The exploratory result analysis shows the efficiency of proposed work as compared to existing work.