A Hybrid Approach Using Case-Based Reasoning and Rule-Based Reasoning to Support Cancer Diagnosis: A Pilot Study.
Recently there has been an increasing interest in applying information technology to support the diagnosis of diseases such as cancer. In this paper, we present a hybrid approach using case-based reasoning (CBR) and rule-based reasoning (RBR) to support cancer diagnosis. We used symptoms, signs, and personal information from patients as inputs to our model. To form specialized diagnoses, we used rules to define the input factors' importance according to the patient's characteristics. The model's output presents the probability of the patient having a type of cancer. To carry out this research, we had the approval of the ethics committee at Napoleão Laureano Hospital, in João Pessoa, Brazil. To define our model's cases, we collected real patient data at Napoleão Laureano Hospital. To define our model's rules and weights, we researched specialized literature and interviewed health professional. To validate our model, we used K-fold cross validation with the data collected at Napoleão Laureano Hospital. The results showed that our approach is an effective CBR system to diagnose cancer.
Department of Computing and Systems, Federal University of Campina Grande, Campina Grande, PB, Brazil.
Renata M
RM
Saraiva
Federal Institute of Paraiba, Monteiro, PB, Brazil.
João
J
Bezerra
Federal Institute of Paraiba, Monteiro, PB, Brazil.
Mirko
M
Perkusich
Department of Computing and Systems, Federal University of Campina Grande, Campina Grande, PB, Brazil.
Hyggo
H
Almeida
Department of Informatics, Federal University of Paraíba, João Pessoa, PB, Brazil.
Clauirton
C
Siebra
D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D009369:Neoplasms / Q000175:diagnosis*; D010865:Pilot Projects; D015203:Reproducibility of Results