Author(s): YB Usman, OI Shittu, MT Aloysius, AO Aliyu and VG Igho

Abstract: The detection of outlier (s) is often thought of as a means to eliminate such aberrant observation(s) from a set of data to avoid anomalies or further analysis. But outliers can be interesting observations in themselves as they can be leading information to certain abnormal condition(s) just like the case of the recent discovery of obesity being a higher risk factor and prognosis to the novel COVID-19 pandemic. This study is focused on detecting outlying values of Body Mass Index (BMI) which are higher risk factors and prognosis of COVID-19 infections using the modified Multihalver technique for detecting outliers. The data of weights in kilogram (kg) and heights in meter (m) was collected from the records of a hospital and the Body Mass Index (BMI) was calculated as weight in kilogram divided by height in meter squared. The distribution of the computed BMI was fitted to the robust criteria of the modified Multihalver technique and the outlying values which are indices of underweight, overweight and obesity were detected as outliers. The study encourages obesity prevention at all ages and recommendation is on appropriate dieting to stay off the possible underlying diseases and in particular the novel COVID-19 pandemic.

DOI: 10.22271/maths.2022.v7.i2b.800

Pages: 99-104 | Views: 686 | Downloads: 22

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