As enormous volume of electronic data increased gradually, searching as well as retrieving essential info from the internet is extremely difficult task.  Normally, the Information Retrieval (IR) systems present info dependent upon the user’s query keywords. At present, it is insufficient as large volume of online data and it contains less precision as the system takes syntactic level search into consideration. Furthermore, numerous previous search engines utilize a variety of techniques for semantic based document extraction and the relevancy between the documents has been measured using page ranking methods. On the other hand, it contains certain problems with searching time.   With the intention of enhancing the query searching time, the research system implemented a Modified Firefly Algorithm (MFA) adapted with Intelligent Ontology and Latent Dirichlet Allocation based Information Retrieval (IOLDAIR) model. In this recommended methodology, the set of web documents, Face book comments and tweets are taken as dataset.  By means of utilizing Tokenization process, the dataset pre-processing is carried out. Strong ontology is built dependent upon a lot of info collected by means of referring via diverse websites. Find out the keywords as well as carry out semantic analysis with user query by utilizing ontology matching by means of jaccard similarity. The feature extraction is carried out dependent upon the semantic analysis. After that, by means of Modified Firefly Algorithm (MFA), the ideal features are chosen. With the help of Fuzzy C-Mean (FCM) clustering, the appropriate documents are grouped and rank them. At last by using IOLDAIR model, the appropriate information’s are extracted. The major benefit of the research technique is the raise in relevancy, capability of dealing with big data as well as fast retrieval.  The experimentation outcomes prove that the presented method attains improved performance when matched up with the previous system.

Healthcare applications in monitoring and managing diseases have undergone rapid development in medical sectors and play an important in observing and controlling diabetes mellitus (DM). DM is a chronic infection that is caused by extreme blood sugar level. The rapid increase of DM world-wide have the effect of gaining attention to predict DM at early stage. Consequently, various technologies have been used to diagnose diabetes at an early stage to avoid major health defects. The most satisfaction in disease prediction and classification methods has been achieved through AI techniques and algorithms in healthcare. The main of the objective of the study is to provide a detail review on DM, the increase of DM around world-wide, datasets used in diabetic prediction, advance techniques and methods applied for disease prediction, and applications and its limitations used in diabetic prediction. The study also provides a detailed review on recent techniques and methods used in disease prediction, which guides the evolution of AI techniques and will provide a well-grounded knowledge of existing methods.

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