Nanotechnology has a tremendous potential to revolutionize agriculture and livestock sector. It can provide new tools for molecular and cellular biology, biotechnology, veterinary physiology, animal genetics, reproduction etc. which will allow researchers to handle biological materials such as DNA, proteins or cells in minute quantities usually nano-liters or pico-liters. Nanotechnology tools like microfluidics, nanomaterials, bioanalytical nanosensors, etc. has the potential to solve many more puzzles related to animal health, production, reproduction and prevention and treatment of diseases. It is reasonable to presume that in the upcoming year’s nanotechnology research will reform the science and technology of the animal health and will help to boost up the livestock production. Nanotechnology will have a profound impact, but not in the immediate future as it is in the early stages of its development and needs to equip scientists, engineers and biologists to work at the cellular and molecular levels for significant benefits in healthcare and animal medicine. But It is reasonable to presume that in the upcoming year’s nanotechnology research will revolutionize animal health and help to boost up livestock production. [Vet World 2009; 2(12.000): 475-477

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In recent years improvement of new and effective medical domain applications has vital role in research. Computational Intelligence Systems (CIS) has profound influence in the enlargement of these effective medical field applications and tools. One of the prevalent diseases that world facing is heart disease. The Computational Intelligence Systems uses input clinical data from different knowledge resources throughout the world and applies this data on different computational intelligence tools that uses sophisticated algorithms. The sophisticated algorithms plays prominent role in the construction of medical clinical analysis tools. These tools may be used as an extra aid for the clinical diagnosis of the diseases for the doctors and clinicians. In this paper a novel method for the diagnosis of heart disease has been proposed using Genetic Algorithms. In this approach an effective association rules are inferred using Genetic Algorithm approach which uses tournament selection, crossover, mutation and new proposed fitness function. The Cleaveland data set is used for the experimentation. This data set is collected from the UCI machine learning repository experimental results are prominent when compared with some of the supervised learning techniques.

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