Detection of Classifiers Using Tidbits

Data mining is the computational process of discovering patterns in a collection of large data sets. It is not based on finding exact patterns alone, but it underlies the importance of tidbits. It is nothing but the collection of accurate data which is for improving the accuracy. Here the prediction of desired pattern is based on the classification algorithm. Classification is to accurately predict the target class for each case in the data. This concept can be applied for medical applications especially for cancer treatment. In this paper classification model could be used to identify the classifiers through symptoms. With respect to medical applications pattern recognition is important for the diagnosis of diseases and identification of each stages of the diseases. Also efficiency and accuracy of decisions will decrease when humans are put into stress and immense work. With the help of Bayes theorem, this work also identifies certain properties of tidbits, which improves the classification accuracy. This concept is used to take accurate decisions when undergoing the treatments. Experimental results can be used to predict the accuracy using the score obtained during the classification and then finds the stage of a patient undertaking cancer treatment assuming medicine as the data set for further prevention.