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Obesity Risk Prediction Using Machine Learning

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dc.contributor.author Ferdowsy, Faria
dc.contributor.author Fatema, Kaniz
dc.contributor.author Yeasmin, Tammim
dc.date.accessioned 2021-07-13T05:26:06Z
dc.date.available 2021-07-13T05:26:06Z
dc.date.issued 2021-01-31
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/5902
dc.description.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%. en_US
dc.language.iso en en_US
dc.publisher Daffodil International University en_US
dc.subject Machine learning en_US
dc.subject Obesity--Diet therapy en_US
dc.subject Health counseling en_US
dc.title Obesity Risk Prediction Using Machine Learning en_US
dc.type Other en_US

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