Dengue infection belongs to the family of virus, Flaviviridae, consisting of four serotypes which spread through the chomp of contaminated Aedes mosquitoes. Around 2.5 billion individuals live in dengue-hazard locales with around 100 million new cases every year around the world. The worldwide predominance of dengue has grown dramatically in later decades. The illness is now endemic in more than 100 nations in Africa, the Americas, the eastern Mediterranean, South East Asia and the western pacific south Asia and the western pacific are the most genuinely influenced. In 1970's only nine nations had encountered DHF plagues, a number which had expanded more than four- crease by 1995[30].Numerous clinical signs are utilized for diagnosing of fever. In any case, it has been an awesome test for the doctors to distinguish the level of hazard in dengue patients utilizing clinical indications. But the disadvantages of clinical procedures make machine learning more powerful in diagnosing of fever in affected patients. Subsequently, this study plans to apply a non-invasive machine learning techniques to help the doctors for ordering the hazard in dengue patients. Conducted a comparison study among Simple Classification and Regression Tree(CART), Multi-layer perception (MLP) and C4.5 algorithms, based on which demonstrating that Simple CART algorithm shows 100% accuracy for classification of affected or unaffected patient.

Because of data mining progress in biomedical and human services networks, precise investigation of clinical data benefits early illness acknowledgment, persistent consideration and network administrations. At the point when the nature of clinical data is inadequate the precision of study is diminished. In addition, various locales display one of a kind appearances of certain territorial maladies, which may brings about debilitating the forecast of illness flare-ups. In the proposed framework, it gives AI calculations to viable forecast of different illness events in sickness visit social orders. It try the adjusted gauge models over genuine medical clinic data gathered. To beat the trouble of inadequate data, it utilize an inert factor model to reconstruct the missing data. It probe a territorial interminable sickness of cerebral dead tissue. Utilizing organized and unstructured data from emergency clinic it use Machine Learning Decision Tree calculation. It predicts likely infections by mining informational indexes and gives recommended specialists and healing arrangements. It will likewise direct the clients by offering tips to carry on with a sound life, some eating routine tips and furthermore value of plants and nourishment things. As far as we could possibly know in the territory of clinical large data examination none of the current work concentrated on the two data types. Contrasted with a few run of the mill gauge calculations, the estimation precision of our proposed calculation arrives at 94.8% with a union speed which is quicker than that of the Decision tree ailment hazard forecast calculation.