Utilize Medical Text Data to Estimate Disease Types by Using Naïve Bayes and ANN Classifier

Mujiono, Sadikin and Deshinta, Arrova Dewi and Purwanto S., Katijan and Ibrohim, Thohari (2021) Utilize Medical Text Data to Estimate Disease Types by Using Naïve Bayes and ANN Classifier. Journal of Data Science, 2021 (01). ISSN 2805-5160

[img] Text
vol.2021_001.pdf

Download (485kB)
Official URL: https://ipublishing.intimal.edu.my/jods.html

Abstract

The primary concept of the hospital is the provision of health services to the community. In many cases, the utilization of information technology to record all hospital activity data can improve hospitals' quality services. However currently, the data is only stored in the database and used as history without further use. Many experiences show that optimizing data usage can greatly assist doctors in making decisions to minimize medical errors. For example, examination data that among others of anamnesis (medical abstract), blood pressure, temperature, and other patient’s symptom data can be used to classify the kind of disease. One of the challenges in medical data utilization is that these data consists of various formats, structured, and unstructured as well. In this study, we address the medical unstructured data format by using Natural Language Processing approach. The combination of its representation results with the structured format data is then used as the dataset to build the model for disease type prediction based on Naïve Bayes and Artificial Neural Network classifier. By using these two algorithms, the results of the classification of the kind of disease. The performed experiments show that the ANN model performs better with the best accuracy average of 89.29% compared to Naive Bayes, which is 80.60 %.

Item Type: Article
Uncontrolled Keywords: Medical abstract, Natural Language Processing, Naïve Bayes, Artificial Neural Network
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Depositing User: Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited: 23 Aug 2021 23:17
Last Modified: 26 Aug 2021 10:20
URI: http://eprints.intimal.edu.my/id/eprint/1522

Actions (login required)

View Item View Item