| Title | Comparison Of Different Machine Learning Methods Applied To Obesity Classification |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | He, Zhenghao |
| Conference Name | 2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) |
| Date Published | aug |
| Keywords | Artificial neural networks, Classification algorithms, component, composability, dimension reduction, dimensionality reduction, machine learning, machine learning algorithms, Obesity, Obesity levels estimation, Prediction algorithms, privacy, pubcrawl, resilience, Resiliency, Support vector machines |
| Abstract | Estimation for obesity levels is always an important topic in medical field since it can provide useful guidance for people that would like to lose weight or keep fit. The article tries to find a model that can predict obesity and provides people with the information of how to avoid overweight. To be more specific, this article applied dimension reduction to the data set to simplify the data and tried to Figure out a most decisive feature of obesity through Principal Component Analysis (PCA) based on the data set. The article also used some machine learning methods like Support Vector Machine (SVM), Decision Tree to do prediction of obesity and wanted to find the major reason of obesity. In addition, the article uses Artificial Neural Network (ANN) to do prediction which has more powerful feature extraction ability to do this. Finally, the article found that family history of obesity is the most decisive feature, and it may because of obesity may be greatly affected by genes or the family eating diet may have great influence. And both ANN and Decision tree's accuracy of prediction is higher than 90%. |
| DOI | 10.1109/MLISE57402.2022.00099 |
| Citation Key | he_comparison_2022 |
Comparison Of Different Machine Learning Methods Applied To Obesity Classification