Diagnosing obesity using classification-based machine learning models Online publication date: Fri, 05-Jan-2024
by Udeechee; T.V. Vijay Kumar; Aayush Goel
International Journal of Electronic Healthcare (IJEH), Vol. 13, No. 3, 2023
Abstract: Obesity has been a major underlying risk for people with chronic diseases and, thus, needs to be diagnosed in the early stages. This requires clinical data related to potential obese individuals to be evaluated for diagnosing obesity levels in individuals. In this paper, classification based machine learning techniques have been applied on such clinical data to design obesity classification models, which would be capable of diagnosing whether an individual is obese or not. Classification techniques, including ensemble techniques, were used to design such obesity classification models. The performance of these obesity classification models was evaluated and compared on metrics such as, accuracy, precision, recall, F1-score and area under the receiver operating characteristic curve. Experimental results showed that the use of ensemble techniques improved the performance of the obesity classification models. Further, amongst the ensemble techniques, the boosting technique based obesity classification model performed the best.
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