Multi-Label Classification for Doctor's Behavioral Pattern Matching During Online Medical Interview using Machine Learning

Safitri Juanita, Diana Purwitasari, I. Ketut Eddy Purnama, Abid Famasya Abdillah, Mauridhi Hery Purnomo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In recent years, many studies on medical texts have attracted the attention of researchers. Medical text studies have few multi-label data targets because it is challenging to understand dependencies between labels. Therefore, this study investigates a collection of medical texts by addressing complex problems in the behavioural pattern of Doctor’s answer text in Online Health Consultation (OHC) by suggesting a pattern of six medical interview functions ranging from fostering doctor-patient relationships to treatment-related behaviours and responding to emotions. There are many proposed MLC methods to solve a multi-label problem. However, this study proposes an MLC model that can improve MLC accuracy, especially in multilingual medical datasets: English and Indonesian. This study proposes 16 MLC models using two feature extraction methods, compares all proposed models, and evaluates model performance using three perspectives. The results show that from 3 perspectives, the MLC model that consistently outperforms other models in the English dataset is a T-BR-RF model (TF/IDF, Binary Relevance, and Random Forest). In contrast, using the Indonesian dataset, the T-BR-AD Model (TF/IDF, Binary Relevance and Adaboost) outperforms other MLC models. The feature extraction method that helps optimize the performance of MLC models is TF-IDF compared to the Word2Vec method.

Original languageEnglish
Pages (from-to)435-452
Number of pages18
JournalInternational Journal on Electrical Engineering and Informatics
Volume15
Issue number3
DOIs
Publication statusPublished - Sept 2023

Keywords

  • Behavioral Pattern Matching
  • Medical Interview Functions
  • Medical Text
  • Multi-label Classification
  • Online Health Consultation

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