Authors :
Jakin KABONGO; MPOVO LUZOLO CLEM’S; PAVODI MANIAMFU; DUMBI KAMANDA LOUISON
Volume/Issue :
Volume 7 - 2022, Issue 12 - December
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3QQvOq8
DOI :
https://doi.org/10.5281/zenodo.7551285
Abstract :
- Obesity and overweight are major risk factors
for a variety of chronic diseases, including
cardiovascular diseases like heart disease and stroke,
which are the main leading causes of most deaths
worldwide. Obesity can also lead to diabetes and its
complications, such as blindness, limb amputations, and
the need for dialysis. Diabetes prevalence has
quadrupled worldwide since 1980. Excess weight can
also cause musculoskeletal disorders such as
osteoarthritis. The objective of this research is to analyze
and predict obesity using machine learning algorithms to
assist clinicians and public health agents to make an
optical decision related to the prevention, the detection,
and the treatment of obesity. Five machine learning
classification models including Random Forest, Support
Vector Machine, Logistic regression, K-nearest Neighbor,
and Ridge Classifier were used for the purpose. These
five models were trained after the Exploratory Data
Analysis and the Data Preprocessing with k-ford crossvalidation, classification report, the confusion matrix,
and the learning curve as metrics. After the training
according to the accuracy performance given by each
model and the learning curve, the Support Vector
Machine was selected and optimized as the final model
with 97% of accuracy.
Keywords :
Obesity, Machine-Learning Algorithms, Premature Deaths
- Obesity and overweight are major risk factors
for a variety of chronic diseases, including
cardiovascular diseases like heart disease and stroke,
which are the main leading causes of most deaths
worldwide. Obesity can also lead to diabetes and its
complications, such as blindness, limb amputations, and
the need for dialysis. Diabetes prevalence has
quadrupled worldwide since 1980. Excess weight can
also cause musculoskeletal disorders such as
osteoarthritis. The objective of this research is to analyze
and predict obesity using machine learning algorithms to
assist clinicians and public health agents to make an
optical decision related to the prevention, the detection,
and the treatment of obesity. Five machine learning
classification models including Random Forest, Support
Vector Machine, Logistic regression, K-nearest Neighbor,
and Ridge Classifier were used for the purpose. These
five models were trained after the Exploratory Data
Analysis and the Data Preprocessing with k-ford crossvalidation, classification report, the confusion matrix,
and the learning curve as metrics. After the training
according to the accuracy performance given by each
model and the learning curve, the Support Vector
Machine was selected and optimized as the final model
with 97% of accuracy.
Keywords :
Obesity, Machine-Learning Algorithms, Premature Deaths
