Current clinical methods base disease detection and identification heavily on the description of symptoms by the patient. This leads to inaccuracy because of the errors that may arise in the quantification of the symptoms and also does not give a complete idea about the presence of any particular disease. The prediction of cellular diseases is still more challenging; for we have no measure on the exact quantity, quality and extremeness. The typical symptoms for these diseases are visible at a later stage allowing the disease to silently progress. This paper provides an efficient and novel way of detection and identification of pancreatitis and breast cancer using a combination of sequence data and information retrieval algorithms to provide the most accurate result. The developed system maintains a knowledge base of the mutations of the diseases causing breast cancer and pancreatitis and thus uses techniques of protein sequence scoring and information retrieval for providing the best match of patient protein sequence with the mutations stored. The system has been tested with mutations available online and gives 98 % accurate results.