Due to rapid growth of on-line information, text classification has become one of key technique for handling and organizing text data. One of the reasons to build taxonomy of documents is to make it easier to find relevant documents, content filtering and topic tracking.LS-SVM is the classifier, used in this paper for efficient classification of text documents. Text data is normally highdimensional characteristic, to reduce the high-dimensionality also possible with SVM.In this paper we are improving classification accuracy and dimensionality reduction of a large text data by Least Square Support Vector Machines along with Singular Value Decomposition.

In this world one of the main sources of death is dependent on coronary illness happens in both men and women. It might cause because of the absence of data or inadequate data gave by the doctor in light of some innovation issue or because the prediction level is low. We have additionally observed the utilization of ML methods in ongoing advancements in different Internet of Things (IoT) fields. Different examinations just give a brief look at anticipating coronary illness utilizing ML methods. In this paper, we are looking at how this hybrid method is better than utilizing a single calculation which gives higher exactness up to 88.7% than contrast with different procedures

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