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This paper presents the efficient machine learning algorithm and techniques used in extracting disease and treatment related sentences from short text published in medical papers. . In this paper better machine learning algorithms and techniques are used for extracting disease treatment relations from various medical related articles. The proposed system gives the user exactly the Disease and Treatment related sentences by avoiding unnecessary information, advertisements from the medical web page namely Medline. For making better medical decisions we can make use of this proposed technique.

The Multi Factor-Naive Bayes classifier based recommendation system is analyzed with respect to the traditional KNN classifier based recommendation system. The classification of the web usage data is done on the basis of the keyword name, keyword count, inbound links and age group of the users. Whereas, in traditional KNN the URL was the only factor that was considered for the purpose of classification. The performance evaluation is done in the terms of RMSE, Error Rate, Accuracy Rate and Precision. The MF-NB is observed to be outperforming the KNN classifier in all respective terms.

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