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Harpaz, R., Callahan, A., Tamang, S., Low, Y., Odgers, D., Finlayson, S., Jung, K., Le Pendu, P. and Shah, N.H. (2014) Text Mining for Adverse Drug Events: The Promise, Challenges, and State of the Art. Drug Safety, 37, 777-790. https://doi.org/10.1007/s40264-014-0218-z

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• TITLE: Classifying Heart Disease in Medical Data Using Deep Learning Methods

  AUTHORS: T. Velmurugan, U. Latha

  KEYWORDS: Named Entity Recognition Algorithm, Neural Network Methods, Whale Optimization Algorithm, F-Measure, Recall, Precision

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.1, January 29, 2021

  ABSTRACT: Recent days, heart ailments assume a fundamental role in the world. The physician gives different name for heart disease, for example, cardiovascular failure, heart failure and so on. Among the automated techniques to discover the coronary illness, this research work uses Named Entity Recognition (NER) algorithm to discover the equivalent words for the coronary illness content to mine the significance in clinical reports and different applications. The Heart sickness text information given by the physician is taken for the preprocessing and changes the text information to the ideal meaning, at that point the resultant text data taken as input for the prediction of heart disease. This experimental work utilizes the NER to discover the equivalent words of the coronary illness text data and currently uses the two strategies namely Optimal Deep Learning and Whale Optimization which are consolidated and proposed another strategy Optimal Deep Neural Network (ODNN) for predicting the illness. For the prediction, weights and ranges of the patient affected information by means of chosen attributes are picked for the experiment. The outcome is then characterized with the Deep Neural Network and Artificial Neural Network to discover the accuracy of the algorithms. The performance of the ODNN is assessed by means for classification methods, for example, precision, recall and f-measure values.