Hybrid Majority Voting: Prediction and Classification Model for Obesity

Abstract

Because it is associated with most multifactorial inherited diseases like heart disease, hypertension, diabetes, and other serious medical conditions, obesity is a major global health concern. Obesity is caused by hereditary, physiological, and environmental factors, as well as poor nutrition and a lack of exercise. Weight loss can be difficult for various reasons, and it is diagnosed via BMI, which is used to estimate body fat for most people. Muscular athletes, for example, may have a BMI in the obesity range even when they are not obese. Researchers from a variety of backgrounds and institutions devised different hypotheses and models for the prediction and classification of obesity using different approaches and various machine learning techniques. In this study, a majority voting-based hybrid modeling approach using a gradient boosting classifier, extreme gradient boosting, and a multilayer perceptron was developed. Seven distinct machine learning algorithms were used on open datasets from the UCI machine learning repository, and their respective accuracy levels were compared before the combined approaches were chosen. The proposed majority voting-based hybrid model for prediction and classification of obesity that was achieved has an accuracy of 97.16%, which is greater than both the individual models and the other hybrid models that have been developed.

Estimation of Obesity Levels through the Proposed Predictive Approach Based on Physical Activity and Nutritional Habits

“…Compared with those of the RF and XGBoost algorithms, the LR model exhibited superior predictions, fortified by optimal hyperparameters and feature selection. Notably, the LR model’s classification results surpassed those of the models employed in other analogous studies [ 44 , 45 , 46 ]. These findings underscore the LR model’s efficacy in the context of obesity classification and prediction.…”

“…They tested seven different machine-learning algorithms on public datasets from the UCI machine-learning repository and compared their accuracy levels. The hybrid model they proposed could predict and classify obesity with an accuracy of 97.16%, which is higher than that of the individual models and other hybrid models [ 44 ]. Another similar article presented three machine-learning methods to forecast obesity in children at age five using real data.…”

Interpretable Machine Learning for Chronic Kidney Disease Diagnosis: A Gaussian Processes Approach

“…In recent years, machine learning (ML) has emerged as a promising tool for disease diagnosis and prediction [8][9][10]. ML algorithms can learn from large datasets and identify patterns that may not be apparent to human analysts [11].…”

Cardiovascular Disease Prediction Using Gradient Boosting Classifier

“…In recent studies on cardiovascular disease prediction, notable contributions have been made by various researchers employing diverse methodologies and GBC has been proved to be useful in classifying medical data [10][11][12][13][14]. Geweid et al advanced the field by constructing identification procedures for cardiovascular diseases using an improved Support Vector Machine (SVM)-based duality optimization approach.…”