Classification of Referral Decision Recommendations in Community Health Centers Using the K-Nearest Neighbor Approach
DOI:
https://doi.org/10.30983/knowbase.v5i2.10137Keywords:
Classification, Decision Support SystemAbstract
management, including determining patient referral decisions at community health
centers. However, these decisions often still depend on the subjective assessment of
medical personnel, resulting in an inaccurate and ineffective process of identifying
diabetes patient management. The purpose and objective of this research and
development is to identify diabetes patient management for referral decision
recommendations at Puskesmas using the K-Nearest Neighbor (KNN) approach to
obtain a more accurate and effective process and results so that Puskesmas can more
quickly provide appropriate follow-up based on patient laboratory test results. The
data used in this study was diabetes patient data at Puskesmas, using variables such
as age, systolic and diastolic blood pressure, glucose tests, and referral to hospitals as
the target class. The results of the research and classification evaluation using the
Confusion Matrix in KNN modeling based on this data showed that the number of
patients included in TP=41, TN=38, FP=1, and FN=4, with an accuracy of 94.02%,
precision of 97.62%, recall of 91.11%, and F1-Score of 94.25%. These values are
categorized as very good because they are able to predict classes correctly at the
modeling stage. Thus, this study is considered feasible as a support for referral
decision recommendations in identifying the treatment of diabetic patients at
Puskesmas
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