To understand the use of computational methods in the given field, particular cases were considered; Medical imaging:Mathematics and Imaging:Medical imaging needs highly trained technicians and clinicians to determine the details of image acquisition as well as to analyze the results. For image analysis, modern image processing techniques have become vital. Artificial systems must be designed to analyze medical datasets either in a partially or even a fully automatic manner. In medical image analysis numerical models should be regarded as the end product. The purpose of the mathematical analysis is to guarantee that the constructed algorithms will behave as desired.Artificial Vision: Artificial Intelligence (AI) was initiated as a field with the goal of creating artificial systems with human-like intelligence. Classical AI however, had been mostly concerned with symbolic representation and reasoning, new subfields were created as researchers embraced the complexity of the goal and realized the importance of sub-symbolic information and awareness. Artificial vision emerged with the more limited goal to mimic human vision with man-made systems. Vision is such a basic aspect of human understanding that it may superficially appear somewhat unimportant, but after decades of research the scientific understanding of biological vision remains extremely fragmented. To date, artificial vision has produced important applications in medical imaging. Many mathematical techniques are employed in artificial vision. However, that does not pretend to simulate biological vision. Artificial vision systems will therefore not be set within the natural limits of human perception. Human vision is naturally two dimensional. To accommodate this limitation, radiologists must resort to visualizing only 2D planar slices of 3D medical images. An artificial system is free of that limitation and can “see” the image in its entirety. An artificial system is free of that limitation and can “see” the image in its entirety. Artificial systems typically complement rather than replace of human experts. Alzheimer’s disease: In our study, protein-protein interaction (PPI) data is introduced to add molecular biological information for estimating signaling trail of Alzheimer’s disease (AD). Combining PPI with gene expression data, important genes are selected by modified linear regression model first. Then, according to the biological researches that inflammation reaction plays a significant role in the generation and deterioration of AD, NF-ЌB (nuclear factor-kappa B), as an important inflammatory factor, has been selected as the beginning gene of the predicting signaling pathway. Based on that, integer linear programming (ILP) model is proposed to reconstruct the signaling pathway between NF-ЌB and AD virulence gene APP (amyloidal precursor protein). The results identify 6 AD virulence genes included in the predicted inflammatory signaling pathway, and a large amount of molecular biological analysis shows the great understanding of the underlying biological process of AD. Plotting the Human Face for Multivariate Data Visualisation by health assessments: This study is conducted to come up with a unique data visualisation system by plotting the human face to notice the complete effects of multivariate data.