| Abstrak/Abstract |
This paper presents a systematic literature review on the
use of machine learning in diagnosing Diabetes Mellitus (DM). The
study examines the application of machine learning algorithms and
datasets in diabetes research. The findings highlight the
effectiveness of Random Forest and the prevalence of the PIMA
Indian dataset in this field. Early detection of diabetes is crucial for
effective management and prevention of complications. However,
challenges such as limited healthcare access and undiagnosed cases
exist. The analysis reveals challenges related to dataset quality,
sensitivity-specificity trade-offs, outliers, and missing data. To
overcome these challenges, future research should expand the
training dataset, incorporate additional parameters, and address
outlier handling techniques. Feature selection methods and careful
consideration of sensitivity-specificity trade-offs are also
recommended. Despite these challenges, machine learning has the
potential to improve diabetes diagnosis and enhance medical care.
This study provides valuable insights for future advancements in
machine learning-based diabetes diagnosis |
