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OriginalPaper | Open access | Published: July 31, 2022

Cross-Validation and Validation Set Methods for Choosing K in KNN Algorithm for Healthcare Case Study

Robbi Rahim, Ansari Saleh Ahmar, Rahmat Hidayat
JINAV: Journal of Information and Visualization, Vol. 3 No. 1 (2022), pp. 57-61 https://doi.org/10.35877/454RI.jinav1557 Published: 2022-07-31
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Abstract

KNN categorization is simple and successful in healthcare. In this research's example case study, the KNN algorithm classified the new record as "Abnormal." The classification method began with choosing K, then calculating the Euclidean distance between the new record and the training set, finding the K nearest neighbors, then classifying the new record based on those K neighbors. The findings show that the KNN algorithm is effective in healthcare and highlight several shortcomings that should be addressed in future study. Weighting variables, choosing the best K value, and handling non-uniform data are these restrictions. The findings show the KNN algorithm's medical potential.

Keywords

KNN AlgorithmEuclidean DistanceHealthcare

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How to Cite

Rahim, R., Ahmar, A. S., & Hidayat, R. (2022). Cross-Validation and Validation Set Methods for Choosing K in KNN Algorithm for Healthcare Case Study. JINAV: Journal of Information and Visualization, 3(1), 57–61. https://doi.org/10.35877/454RI.jinav1557