In this paper the methodology of designing a genomic-based point-of-care diagnostic system composed of a microfluidic Lab-On-Chip, algorithms for microarray image information extraction and knowledge modeling of clinico-genomic patient data is presented. The data are processed by genome wide association studies for two complex diseases: rheumatoid arthritis and multiple sclerosis. Respecting current technological limitations of autonomous molecular-based Lab-On-Chip systems the approach proposed in this work aims to enhance the diagnostic accuracy of the miniaturized LOC system. By providing a decision support system based on the data mining technologies, a robust portable integrated point-of-care diagnostic assay will be implemented. Initially, the gene discovery process is described followed by the detection of the most informative SNPs associated with the diseases. The clinical data and the selected associated SNPs are modeled using data mining techniques to allow the knowledge modeling framework to provide the diagnosis for new patients performing the point-of-care examination. The microfluidic LOC device supplies the diagnostic component of the platform with a set of SNPs associated with the diseases and the ruled-based decision support system combines this genomic information with the clinical data of the patient to outcome the final diagnostic result.