Patient Diagnosis Classification based on Electronic Medical Record using Text Mining and Support Vector Machine

M. Jamaluddin, Adhi Dharma Wibawa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Electronic Medical Record (EMR) is an important element of information technology in healthcare sector. EMR is an electronic record containing health-related information on patients that can be created and managed by authorized physician and staff in a healthcare service organization. EMR is a framework for determining diagnosis and treatment. EMR has free text and unstructured format which makes it more difficult to extract the hidden information as a decision support system. This study performs classification from Indonesian EMR for clinical decision support system (CDSS) in classifying patient diagnosis using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction and Support Vector Machine (SVM) for classifier method. SVM is a powerful algorithm in high-dimensional data such as in textual data processing. The focus diagnoses classified in this paper are tuberculosis, cancer, diabetes mellitus, hypertension, and chronic kidney which have high prevalence rates in Indonesia. The model is built by considering the kernel function and the use of stopword removal or without stopword removal. The result showed that TF - IDF and SVM method could be used effectively to predict diagnosis with stop word removal. Classification performance increased with stopword removal on all SVM kernels with accuracy in linear kernel 89.91 %, polynomial kernel 90.58%, RBF kernel 90.75%, and sigmoid kernel 91.03%..

Original languageEnglish
Title of host publicationProceedings - 2021 International Seminar on Application for Technology of Information and Communication
Subtitle of host publicationIT Opportunities and Creativities for Digital Innovation and Communication within Global Pandemic, iSemantic 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages243-248
Number of pages6
ISBN (Electronic)9781665428040
DOIs
Publication statusPublished - 18 Sept 2021
Event2021 International Seminar on Application for Technology of Information and Communication, iSemantic 2021 - Semarang, Indonesia
Duration: 18 Sept 202119 Sept 2021

Publication series

NameProceedings - 2021 International Seminar on Application for Technology of Information and Communication: IT Opportunities and Creativities for Digital Innovation and Communication within Global Pandemic, iSemantic 2021

Conference

Conference2021 International Seminar on Application for Technology of Information and Communication, iSemantic 2021
Country/TerritoryIndonesia
CitySemarang
Period18/09/2119/09/21

Keywords

  • Electronic Medical Record
  • Support Vector Machine
  • Text Mining

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