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

• Main Authors: Mujiono, Sadikin, Deshinta, Arrova Dewi, Purwanto S., Katijan, Ibrohim, Thohari
• Format: Article
• Language: English
• Published: INTI International University 2021
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software
• Online Access: http://eprints.intimal.edu.my/1522/1/jods2021_1.pdf; http://eprints.intimal.edu.my/1522/; https://ipublishing.intimal.edu.my/jods.html

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 %.