Suwarno, Suwarno and Murnaka, Nerru Pranuta and Prasetyo, Puguh Wahyu (2023) Performance comparison of machine learning algorithms for predicting obesity level.
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Abstract
Obesity problems have actually come to be a worldwide epidemic that has increased since 1980, with significant repercussions for health and wellness in young adults, adults, and youngsters. Obesity problems are an issue that has actually been expanding steadily which is why daily appear new studies entailing youngsters’ excessive weight, specifically those looking for influence elements as well as exactly how to predict the appearance of the condition under these elements; for this reason, early detection is called for. Data mining and also machine learning (ML) algorithms approaches are made use of in obesity problems forecast in our research. We made use of the Obesity Level dataset for our study, accumulated from the UCI Machine Learning Repository. The dataset includes information about 638 patients as well as their matching 17 attributes. We made use of nine ML algorithms on the dataset to predict obesity problems. We found that the model with Logistic Regression algorithm is well on obesity level prediction. The result validated Logistic Regression algorithm has the best performance of accuracy (100%), sensitivity (100%), specificity (100%), as well as AUC (1). The Logistic Regression model was selected because to its best performance, best gain, and fastest total time.
| Item Type: | Artikel Umum |
|---|---|
| Subjects: | Q Science > QA Mathematics |
| Depositing User: | puguh prasetyo |
| Date Deposited: | 11 Aug 2023 06:55 |
| Last Modified: | 02 Sep 2023 08:53 |
| URI: | http://eprints.uad.ac.id/id/eprint/44192 |
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