摘要

Most screening tests for Diabetes Mellitus (DM) in use today were developed using electronically collected data from Electronic Health Record (EHR). However, developing and under-developing countries are still struggling to build EHR in their hospitals. Due to the lack of HER data, early screening tools are not available for those countries. This study develops a prediction model for early DM by direct questionnaires for a tertiary hospital in Bangladesh. Information gain technique was used to reduce irreverent features. Using selected variables, we developed logistic regression, support vector machine, K-nearest neighbor, Naïve Bayes, random forest (RF), and neural network models to predict diabetes at an early stage. RF outperformed other machine learning algorithms achieved 100% accuracy. These findings suggest that a combination of simple questionnaires and a machine learning algorithm can be a powerful tool to identify undiagnosed DM patients.
原文英語
主出版物標題Advances in Informatics, Management and Technology in Healthcare
編輯John Mantas, Parisis Gallos, Emmanouil Zoulias, Arie Hasman, Mowafa S. Househ, Marianna Diomidous, Joseph Liaskos, Martha Charalampidou
發行者IOS Press BV
頁面409-413
頁數5
ISBN(電子)9781643682907
DOIs
出版狀態已發佈 - 2022

出版系列

名字Studies in Health Technology and Informatics
295
ISSN(列印)0926-9630
ISSN(電子)1879-8365

ASJC Scopus subject areas

  • 生物醫學工程
  • 健康資訊學
  • 健康資訊管理

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