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Comparison of Regression, K-Nearest Neighbors (KNN) and Multi-Layer Perceptron (MLP) Models for the Prediction of Weight, Gender and Body Mass Index Status
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 29 |
| Year of Publication: 2023 |
| Authors: Md Jabed Hosen, Iqbal Ahmed |
10.5120/ijca2023923040
|
Md Jabed Hosen, Iqbal Ahmed . Comparison of Regression, K-Nearest Neighbors (KNN) and Multi-Layer Perceptron (MLP) Models for the Prediction of Weight, Gender and Body Mass Index Status. International Journal of Computer Applications. 185, 29 ( Aug 2023), 23-30. DOI=10.5120/ijca2023923040
Abstract
Maintaining good health is challenging, and weight is a significant indicator of health. Individuals with underweight, overweight, or obese tend to suffer from major health issues like diabetes, heart disease, high blood pressure, and cancer. To avoid and control these issues, precise estimates of weight, gender, and BMI status are crucial. This article uses machine learning algorithms like Regression, K-Nearest Neighbor, and Multi-layer Perceptron to predict weight, gender, and body mass index status. The results show that linear regression models can predict weight based on height with around 85% accuracy. KNN performs best when considering gender, with an accuracy score of 91.04%. The MLP model is the most effective in predicting gender from height and weight, with an accuracy rating of 92.07%. Finally, the MLP model surpasses other models in predicting BMI status based on height and weight, scoring 97% accuracy. This study is expected to be beneficial to medical science and public health care.
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