Using Machine Learning Method for classification Body Mass Index for clinical decision

Abstract

Background: Body mass index (BMI) is a good method for measure the overweight and obesity among people. The aim of this study was to develop a machine learning method to classification of BMI for clinical application.Methods: In this study we used the dataset of 1316 people who selected randomly from all area of Ardabil city. Dataset included demographic and anthropometric data. Classification algorithms such as Random forest (RF), Gaussian Naïve Bayes (GNB), Decision Tree (DT), Support-Vector Machines (SVM), Multi-layer Perceptron (MLP), K-nearest neighbors (KNN) and Logistic Regression (LR) were used for classification of people based on BMI data. The performance of algorithms were evaluated with Precision, Recall, Mean Squared Errors (MSE) and Accuracy. All programing done in python.3.7 in Jupyter Notebook. Results: According to 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 was 0.93, 0.86, 0.99, 0.82, 100, 0.82 and 0.99 respectively. Also, the accuracy of RF, GNB, DT, SVM, MLP, KNN and LR were 95%, 83%, 100%, 82%, 100%, 82% and 100 %. Conclusion: In compare classification algorithms results showed that, the LR , MLP and DT had the higher full accuracy than other algorithms in detection of people at-risk.