Naïve Bayes and K-Nearest Neighbor Algorithms Performance Comparison in Diabetes Mellitus Early Diagnosis

Authors

  • Haviluddin Mulawarman University
  • Novianti Puspitasari Universitas Mulawarman
  • Aji Ery Burhandeny Universitas Mulawarman
  • Andi Dhiya Awalia Nurulita Universitas Mulawarman
  • Dinnuhoni Trahutomo Universitas Mulawarman

DOI:

https://doi.org/10.3991/ijoe.v18i15.34143

Keywords:

classification, naïve bayes, KNN, diabetes mellitus, confusion matrix

Abstract


Diabetes Mellitus (DM) is a chronic disease that occurs when the body cannot effectively use the insulin it produces. The use of artificial intelligence (AI) can provide a means to diagnose. This study aims to obtain the best classification of the Naïve Bayes (NB) and K-Nearest Neighbors (KNN) methods so that accurate results are obtained in diagnosing DM disease using a dataset originating from The Abdul Moeis Hospital, Samarinda, East Kalimantan, Indonesia. The results showed that the KNN performed better in accuracy, precision, and specificity with an Area Under the Curve (AUC) value 10% higher than NB. Overall, KNN obtained a better recall compared to the NB in order to DM diagnosis.

Covr iJOE Vol. 18 No. 15 (2022)

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Published

2022-12-06

How to Cite

Haviluddin, Puspitasari, N., Ery Burhandeny, A., Dhiya Awalia Nurulita, A., & Trahutomo, D. (2022). Naïve Bayes and K-Nearest Neighbor Algorithms Performance Comparison in Diabetes Mellitus Early Diagnosis. International Journal of Online and Biomedical Engineering (iJOE), 18(15), pp. 202–215. https://doi.org/10.3991/ijoe.v18i15.34143

Issue

Vol. 18 No. 15 (2022)

Section

Papers

License

Copyright (c) 2022 Haviluddin Haviluddin, Novianti Puspitasari; Aji Ery Burhandeny; Andi Dhiya Awalia Nurulita; Dinnuhoni Trahutomo

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