Phyton-Based Machine Learning Algorithm To Predict Obesity Risk Factors In Adult Populations

Authors

  • Mari Rahmawati Universitas Bina Sarana Informatika
  • Ade Fitria Lestari Universitas Bina Sarana Informatika
  • Sri Hardani Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.31294/p.v26i1.3242

Keywords:

Obesity, Classification, K-Nearest Neighbor, Decision Tree, Naïve Bayes

Abstract

Obesity is a serious health problem because it can lead to a variety of diseases. Adults are prone to obesity due to several factors such as age, physical activity, weight, diet, gender, lifestyle and so on. Machine Learning as one of the methods for predicting and classifying factors of obesity especially in the adult population. In machine learning, there are various types of algorithms that can be used to classify data. In this study, using the K-Nearest Neighbor, Decision Tree and Naïve Bayes algorithms, 2111 datasets were used and processed using the Phyton programming language. The results were obtained from the comparison of the three algoritms with the highest accuracy of 93.6%, the Decision Trees with 79.6% and the Naïv Bayes with 60%.

Downloads

Published

2024-03-29

How to Cite

Rahmawati, M., Lestari, A. F. ., & Hardani, S. (2024). Phyton-Based Machine Learning Algorithm To Predict Obesity Risk Factors In Adult Populations. Paradigma - Jurnal Komputer Dan Informatika, 26(1), 51-57. https://doi.org/10.31294/p.v26i1.3242

Issue

Vol. 26 No. 1 (2024): March 2024 Period

Section

Articles

License

Copyright (c) 2024 Mari Rahmawati, Ade Fitria Lestari, Sri Hardani

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Articles published under the PARADIGMA Journal are made freely available online immediately upon publication, without subscription barriers to access. Authors who choose to participate in the PARADIGMA Journal initiative and pay to have their paper freely available online will be asked to sign an open-access license agreement.

Our PARADIGMA Journal implements are CC-BY-SA, This license permits users to use, reproduce, disseminate or display the article provided that the author is attributed as the original creator and that the reuse is restricted to non-commercial purposes i.e. research or educational use.