Publication

Abstract : In the advanced era, data decides the life which leads to future prediction also, such valuable contents are not that much easy to handle and classify. There is an unimaginable amount of data available around us in many forms about many contents. One such form of data is text that in turn paves the way for many understandings of the present and when it analyzed properly and precisely it helps us to predict the future in many areas that include medical, industrial safety, supply chain management etc. Text classification plays a most prominent role in Natural Language Processing. The major step in analyzing the data is to classify them accordingly in which a greater knowledge is in need to do it and convert it in to code for that a brief categorization understanding is in need. If the above said point was justified properly then the text can automatically categorize text into several predetermined categories. Categorizing text could eliminate the necessity of sorting data manually though it is a intensive and expensive process. The above said process was not that easy and there are many algorithms that work for it to classify it in precise manner like SVM – Support vector mechanism, KNN – k-nearest neighbor algorithm, etc. The project suggested a Heart Disease Data Classification approach which combines advanced machine learning techniques particularly the classic RNN and MLP to forecast the possibility ofacquiring heart-oriented conditions. Where MLP represents a incredibly efficient ways to classify utilizing the Deep advanced Learning in ANN. This proposed technique used deep advanced learning to attain precise outcomes with acceptable errors. This advanced learning approach for text recognition depends on standard performance measures that includes metrics viz accuracy, precision, recall and f1-score.