DISEASE PREDICTION SYSTEM USING SEQUENCE ALIGNMENT

Cancer is one of the most dreaded ailments on the planet. It has expanded shockingly and bosom disease happens in one out of eight ladies, the forecast of malignancies assumes fundamental role in uncovering human genome, yet in addition in finding powerful counteraction and treatment of tumors. This paper proposes a novel technique that can foresee the disease by mutations. We will compare the patient's protein and the gene's protein of disease and in the event that there is distinction between these two proteins, at that point we can say there is malignant transformations. We found that LCS algorithm is a simple and efficient algorithm which does sequence alignment on a pair of sequences. Furthermore, we did a detailed study on machine learning approaches and determine the best approach for training and testing the dataset. We chose Support Vector Machines (SVM) since it gave the best results of about 98% accuracy. Finally, we created a user-friendly website that allows users to give an input sequence and results an output whether the given sequence is malignant orbenign.