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Submitted: 26 Jun 2022
Accepted: 23 Aug 2022
ePublished: 27 Aug 2022
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J Renal Endocrinol. 2022;8: e17072.
doi: 10.34172/jre.2022.17072
  Abstract View: 37
  PDF Download: 15

Original Article

Using machine learning method for classification body mass index of people for clinical decision

Firouz Amani 1 ORCID logo, Alireza Mohamadnia 2 ORCID logo, Paniz Amani 3, Soheila Abdollahi-Asl 4, Mohammad Bahadoram 5* ORCID logo

1 Department of Community Medicine, School of Medicine, Ardabil University of Medical Science, Ardabil, Iran.
2 Department of Basic Science, School of Medicine, Ardabil University of Medical Science, Ardabil, Iran.
3 Student in Electronic Engineering, Tabriz University, Tabriz, Iran.
4 Ardabil Azad University, Ardabil, Iran.
5 Thalassemia and Hemoglobinopathy Research Center, Health Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
*Corresponding Author: *Corresponding Author: Mohammad Bahadoram, Email: mohammadbahadoram@yahoo.com, , Email: bahadoram.m@ajums.ac.ir

Abstract

Introduction: Body mass index (BMI) is an acceptable method to measure overweight and obesity among the population.

Objectives: The aim of this study was evaluating the application of machine learning algorithms for classifying body mass index for clinical purposes.

Patients and Methods: In this descriptive study, we selected the dataset of 1316 people who selected randomly from all area of Ardabil city in Iran. Dataset included demographic and anthropometric data. Classification algorithms such as random forest (RF), Gaussian Naive Bayes (GNB), decision tree (DT), support vector machines (SVM), multi-layer perceptron (MLP), K-nearest neighbors (KNN) and logistic regression (LR) with 10-fold cross-validation were conducted to classify the data based on BMI. The performance of algorithms was evaluated with precision, recall, mean squared errors (MSE) and accuracy indices. All programing done by Python 3.7 in Jupyter Notebook.

Results: According to the BMI, 603(45.8%) of all samples were normal and 713 (54.2%) were at-risk. The precision of RF, GNB, DT, SVM, MLP, KNN and LR for people at risk were 0.93, 0.86, 0.99, 0.82, 100, 0.82 and 0.99 respectively. Additionally, the accuracy of RF, GNB, DT, SVM, MLP, KNN and LR were 95%, 83%, 100%, 82%, 100%, 82% and 100 %.

Conclusion: The comparison of the classifying algorithms showed that, the LR, MLP and DT had the higher accuracy than the other algorithms in detecting of people at-risk.


Citation: Amani F, Mohammadnia A, Amani P, Abdollahi-Asl S, Bahadoram M. Using machine learning method for classification body mass index of people for clinical decision. J Ren Endocrinol. 2022;8:e17072 doi: 10.34172/jre.2022.17072.
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