Improving Health Status Prediction by Applying Appropriate Missing Value Imputation Technique

Shaira Tabassum, Nuren Abedin, Rafiqul Islam Maruf, Mostafa Taufiq Ahmed, Ashir Ahmed

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

The presence of missing information in health data is a common occurrence, especially in remote healthcare systems. Lack of data in the medical domains reduces the representativeness of the samples, creates biased estimations, and leads to improper conclusions. These missing values need to be handled efficiently by selecting an appropriate imputation technique. This paper aims to find a suitable imputation technique for remote healthcare data. We use our Portable Health Clinic (PHC) dataset which was collected over 12 long years from different locations in Bangladesh and it was found that 20% of data items were missing. We carried out a comparative analysis among eight missing value handling methods by applying these methods to five state-of-the-art machine learning models with PHC Healthcare Dataset. The imputation performance of each case is evaluated based on accuracy and f1-score. The Multiple Imputation by Chained Equations (MICE) imputation has achieved the highest accuracy and f1-score in all of the cases. Thus, this study demonstrates MICE as the best performing missing value imputation technique with any composition of machine learning process and algorithms.

本文言語英語
ホスト出版物のタイトルLifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
出版社Institute of Electrical and Electronics Engineers Inc.
ページ345-348
ページ数4
ISBN(電子版)9781665419048
DOI
出版ステータス出版済み - 2022
イベント4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022 - Osaka, 日本
継続期間: 3月 7 20223月 9 2022

出版物シリーズ

名前LifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies

会議

会議4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022
国/地域日本
CityOsaka
Period3/7/223/9/22

!!!All Science Journal Classification (ASJC) codes

  • 農業および生物科学(その他)
  • 人工知能
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識
  • 生体医工学
  • 器械工学
  • 教育

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