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@article{145548,
        author = {Ch.Sireesha and G.Ananthnath},
        title = {Forecast the Type of  Fever by using decision tree through diagnosis},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {10},
        pages = {401-403},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145548},
        abstract = {Generally there is a lot of problems in identifying the type of fever. In this project we developed a new system to predict the type fever in early stages. In the existing system for missing values we used automated data mining missing value imputation techniques. These techniques may fill approximate or wrong values in many cases. Due to this one final result may affected.  In the existing methods for feature selection they used algorithms. This technique also may choose less important attributes. Due to this one the processing time may increases. It may affect final results also. By considering all these techniques we use artificial neural networks (ANN) based on Humidity, rainfall and temperature. To overcome all these problems we go for proposed model. Proposed system consists of three important steps: 
a) Manual missing value imputation method is applied that makes the data consistent.
 b) We take the expert opinion for selecting most influential attributes for fever also we done internet survey. c) For accurate prediction of fever we use decision tree model. The expert system is developed using java. This expert system gives good results when compared to existing system.
},
        keywords = {Fever, Expert system, Neural Network, Prediction.},
        month = {},
        }