Identifying sub-groups of the obese from national health and nutritional status survey data using machine learning techniques

"Profiling Obese Subgroups in National Health and Nutritional Status Survey Data using Machine Learning Techniques – A Case Study from Brunei Darussalam"

“…BMI measures it in kg/m2 [2,4,5]. It further defined the Overweight and obesity for adults as follows; BMI more than equal to 25 kg/m2 for Overweight; and BMI more than equal to 30 kg/m2 for Obese [2,[6][7][8]. Obesity has been further classified as BMI more than equal to 30kg/m2.…”

“…However, this classification does not completely consider the population-level heterogeneity and cannot identify the variations among obese individuals. There is evidence of the association of obesity with other factors, including demographics, nutritional habits, and individuals' physical activity [7,8]. In our case, Body Mass Index (BMI) was calculated and inserted into the dataset as a variable feature to study the characteristics of obese people and the prevalence of obesity [1,6].…”

“…In our case, Body Mass Index (BMI) was calculated and inserted into the dataset as a variable feature to study the characteristics of obese people and the prevalence of obesity [1,6]. This survey also used the same variable features like demographic status, diet patterns, and physical activity together with the history of raised blood pressure, diabetes, and raised cholesterol with BMI measurements as done in the studies carried out in the past [8].…”

“…The study's focus has been on the prevalence of obesity and the lifestyle factors affecting it. Past studies have shown that it has been one of the major risk factors causing other non-communicable diseases such as diabetes and cardiovascular problems [7][8][9]. The threat of obesity related NCDs, especially chronic kidney diseases (CKD), is preventable by educating the population about the risks of being obese and prevention through a healthy lifestyle [5].…”

“…The choice of the technique was evident and applied to the NHANSS data set as discussed in Section 1.4 to generate meaningful patterns as far as obesity prevalence in the community was concerned. The review of the obtained results may help the health care industry to classify the characteristics of patients for a particular disease and to use that information to improve the protocols and procedures for the better treatment of patients by the clinicians and, most importantly, for the betterment of humanity in general [8,16,17].…”