Data mining techniques can be applied in various fields such as Information Retrieval, Business analytics, Medicine and many more. This paper deals with medical field which mainly focuses on liver disease diagnoses. The aim of this study is to implement different classification algorithms on Indian Liver Patient Dataset (ILPD) using WEKA in order to get proper prediction of liver disorders. Feature selection is carried out on the dataset. Pre-processing is carried out to pre-process and cluster the data. K means clustering algorithm is used for pre-processing the data. The clustered data is further applied to various classification algorithms such as Naive Bayes, Ada Boost, J48, Bagging and Random Forest. A comparison is carried out considering performance measures such as Accuracy, Error rate, Precision, Recall and F measure. On the basis of comparison, the results are concluded. Random Forest algorithm provides best performance among all.

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Semantics-based information representations such as ontologies are found to be very useful in repeatedly generating important factual questions. Formative the difficulty-level Of these system generated questions is helpful to successfully make use of them in various learning and specialized applications. The accessible approaches for result the difficulty-level of factual questions are very simple and are limited to a few basic principles. We suggest a new tactic for this problem by considering an edifying theory called Item Response Theory (IRT).In the IRT, facts skill of end users (learners) are considered for assigning difficulty levels, because of the assumptions that a given question is apparent differently by learners of various proficiencies. We have done a detailed study on the features/factors of a question statement which could perhaps determine its difficulty-level for three learner categories (experts, intermediates, and easy).