The Application of Agglomerative Hierarchical Clustering for Obesity Classification

Obesity is the excessive accumulation of fat in the body which adversely affects the health and wellbeing of the individual. It is a chronic and non-communicable disorder that poses socio-cultural, psychological, clinical and public health challenges. The aim of this study is to apply Agglomerative Hierarchical Clustering (AHC) to classify obesity and to develop a model employing Logistic regression analysis for the prediction of obesity taking advantage of the relationship between Body Mass Index (BMI), Age, Waist Circumference (WC), High-Density Lipoprotein (HDL)-cholesterol and Low-Density Lipoprotein (LDL)-cholesterol. This Study was a work–site based cross sectional study carried out on one hundred and twenty (120) workers at Judiciary Service Commission, Owerri, Imo State, Nigeria. The Questionnaire was designed to address the background information of the respondents with respect to gender, age, job title, department and address. The respondents were anthropometrically examined and their lipid profile was estimated using the enzymatic colorimetric method. Data were analysed using the Shapiro-wilks test of normality, Agglomerative Hierarchy Cluster (AHC) analysis and Logistic regression analysis. These analyses were facilitated using XLSTAT 2016 statistical tool. On the application of the Agglomerative Hierarchical Cluster Analysis obesity was classified into Clusters 1, 2 and 3 with the majority of the obesed respondents being in Cluster 1. The respondents in Cluster 1 belonged to the obesity class of overweight, while respondents in Cluster 2 are of normal weight and finally respondents in Cluster 3 belonged to obese class 1. A predictive model was developed based on Logistic regression analysis which showed a strong positive correlation between obesity and HDL-cholesterol. The high profile of cardiovascular risks identified in the study could be addressed through the provision of occupational health services of which the ultimate goal should be the maintenance of urgent comprehensive health surveillance.

Author(s) Details

Charles C. Onoh
Centre for Occupational Health, Safety and Environment, University of Port Harcourt, Nigeria.

Professor Ify L. Nwaogazie
Department of Civil and Environmental Engineering, University of Port Harcourt, Nigeria.

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