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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Hapizah Mohd Nawawi
Universiti Teknologi MARA (UiTM)
Computer Science & IT Engineering

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Determination of the obesity prevalence and its associated factors among community in Selangor, Malaysia: an ordinal logistic regression approach Noraznie Nordin; Zalina Zahid; Zaliha Ismail; Siti Munira Yassin; Hapizah Mohd Nawawi; Siti Aida Sheikh Hussin
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 1: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i1.pp428-434

Abstract

Obesity is becoming an epidemic globally as it has been closely linked with a wide variety of chronic diseases. The identification of associated factors for obesity occurrences is still the main interest of many researchers. However, there has been extensive disagreement among researchers over possible factors associated with obesity which commonly involve the demographic factors, socioeconomic status (SES) and environmental factors. Biomarkers are also considered as important possible factors linked with the prevalence of obesity but investigations looking into their associations are still lacking. Therefore, it is important to examine factors that are associated with obesity using biomarkers and common factors to get detailed perspectives on obesity prevalence. The objectives of this study are to determine the prevalence of obesity and to examine the association between the common factors and biomarkers with obesity among community in Selangor, Malaysia. The results showed that the prevalence of obesity among participants was 49% (N=498) and Ordinal regression model with Cauchit built-in link function was the best fitted model to predict obesity. Meanwhile, three types of common factors (i.e. older age, being female and Malay ethnic) and one type of biomarker (i.e. high glucose level) were found to be significantly associated with obesity.
Cross-checked screening application for reliable categorisation of familial hypercholesterolaemia: design and development of the prototype Marshima Mohd Rosli; Muthukkaruppan Annamalai; Noor Alicezah Mohd Kasim; Chua Yung-An; Hapizah Mohd Nawawi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp704-713

Abstract

The paper describes the development of a computer-based familial hypercholesterolemia (FH) screening application (FH CatScreen©). The application facilitates automatic scoring and categorisation of patients by medical practitioners based on four well-known FH diagnostic criteria. In the absence of a FH diagnostic criterion for Malaysian population, these four diagnostic criteria are commonly used criteria to classify patients FH severity levels to manage early interventions. We applied an adaptive software development approach comprising planning, development and validation phases to develop FH CatScreen©. A user study involving thirty medical practitioners was conducted to evaluate the effectiveness and usability of FH CatScreen©. The study showed that FH CatScreen© was able to provide a more correct, faster and better-informed assessment compared to the traditional paper-based method. The study further showed that FH CatScreen© has a good degree of performance and acceptance by the participants. The participants indicated that the simultaneous use of the four diagnostic criteria in FH CatScreen© has assisted them to compare the outcomes of each of the criterion side-by-side. It allowed them to decide on the severity of patient condition with high confidence. FH CatScreen© has demonstrated its expediency and efficacy in collecting the data on FH incidence and prevalence in Malaysia.
Co-Authors Chua Yung-An Marshima Mohd Rosli Muthukkaruppan Annamalai Noor Alicezah Mohd Kasim Noraznie Nordin Siti Aida Sheikh Hussin Siti Munira Yassin Zaliha Ismail Zalina Zahid