Abstract:
Obesity is dangerous for health. Nowadays it has become a threat to people all over the
world. The range of obese people in Bangladesh is also increasing rapidly and especially
young people are being attacked because of their addiction to junk food. Obesity has a
negative impact on our body and life. We have to keep an eye on the topic of people being
obese and their health and body getting affected by it which can make them have many
deadly diseases and drag to death also. We need to stay away from an unhealthy lifestyle,
should stop overeating, should move our body enough, and try to avoid being obese. We
will predict the risk of becoming obese with machine learning. First, we study some related
papers, journals, and online articles then we talk to doctors and obese people; we find some
common factors that are related to become obese. Then we collect data based on those
factors, such as genetics, age, height, weight, diet, gender, profession, health ability, mental
pressure, trauma, daily life routine, etc. We collect data from both types of people who are
obese and who are not obese. We have two outcomes and the outcomes are ‘yes’, and ‘no’.
These will describe that if there is any extra fat is accumulating on the body or not. After
data collection, we processed all the data and created a processed dataset. We applied
machine-learning algorithms to our processed dataset. Since machine learning, artificial
intelligence, and deep learning used in various predictions and detection systems. We used
k-nearest neighbor, logistic regression, support vector machine (SVM), naïve Bayes,
random forest, adaptive boosting (ADA boosting), decision tree, multilayer perceptron
(MLP), and gradient boosting classifier. In our work, out of the nine algorithms, logistics
regression gave the best performance based on accuracy and the accuracy of logistic
regression was 97.03%.