PM02: LEARNING DIAGNOSTIC CLASSIFIER(LDC) BASED ON MODULAR CLUSTERING,
MAMDANI FUZZY MODEL AND CO-EVOLUTIONARY ALGORITHM
Functions & goal
LDC-generic block diagram is shown in Figure 1 is implemented in two modes. In the first (OFF-line mode), the model can be trained/learned by microarray data sets available in NCBI, GEOSOFT, GEMS, UCI and other databases, or with the available data from clinic. In the second mode (ON-line mode) the subsystem will receive real data whose sources are microarray data obtained from samples of the treated patient.
The application of sophisticated (fusion) methods will optimize the microarray input data set using Co-evolutionary Fuzzy Logic (CFL) – Figure 2.
Figure 1. Learning diagnostic classifier-generic block diagram
The output (responses) from this model will provide predictive cancer diagnosis and predictive biomarkers that may improve the accuracy of the classical diagnostic techniques. The LDC is the first level of support in estimating patient survival scenario.
Figure 2. Diagnosis and biomarkers block of LDC