A Frame Work for Classification of Multi Class Medical Data based on Deep Learning and Naive Bayes Classification Model

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N. Ramesh 1,* G. Lavanya Devi 1 K Srinivasa Rao 1

1. Dept of CS&SE, AU College of Engineering, Andhra University, Visakhapatnam, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2020.01.05

Received: 2 Jul. 2019 / Revised: 19 Aug. 2019 / Accepted: 28 Oct. 2019 / Published: 8 Feb. 2020

Index Terms

Health records, MIMIC-III, Deep learning, auto-encoder


From the past decade there has been drastic development and deployment of digital data stored in electronic health record (EHR). Initially, it is designed for getting patient general information and performing health care tasks like billing, but researchers focused on secondary and most important use of these data for various clinical applications. In this paper we used deep learning based clinical note multi-label multi class approach using GloVe model for feature extraction from text notes, Auto-Encoder for training based on model and Navie basian classification and we map those classes for multi- classes. And we perform experiments with python and we used libraries of keras, tensor flow, numpy, matplotlib and we use MIMIC-III data set. And we made comparison with existing works CNN, skip-gram, n-gram and bag-of words. The performance results shows that proposed frame work performed good while classifying the text notes.

Cite This Paper

N. Ramesh, G. Lavanya Devi, K Srinivasa Rao, "A Frame Work for Classification of Multi Class Medical Data based on Deep Learning and Naive Bayes Classification Model", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.12, No.1, pp. 37-43, 2020. DOI:10.5815/ijieeb.2020.01.05


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