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Prevalence of obesity in Malaysia is one of the highest among the Asian countries and it is one of the risk factors for cardiovascular disease which is the main cause of death in Malaysia. This paper aims to investigate the relationship between obesity and mortality rate and find its association with sociodemographic factors such as education and household income. This time-series study used secondary data from World Bank data and Household Income Survey (HIS), and it used annual data from 1990 to 2019. ADF test is used to test the stationarity. Simple linear and multiple linear regression were used in this paper to study the relationship between mortality with obesity, education, and household income. Multicollinearity, heteroskedasticity and R-squared were used to define the best regression model in this study. This study found that mortality was positively associated with obesity. Besides, the findings also shows that there was an inverse relationship between obesity and education, meanwhile positive relationship appears between obesity and household income. [ABSTRACT FROM AUTHOR]

The global average life expectancy has increased astonishingly. However, problems such as household income inequality, high unemployment and the continuous impact of economic growth have prevented Malaysia's life expectancy from increasing. This paper studies the effect of macroeconomic variables on life expectancy in Malaysia from 1991–2019. The data was collected from the World Bank and the Department of Statistics Malaysia. For this purpose, the analysis of the study is to use the Autoregressive Distributed Lag (ARDL) approach. The results show that household income variables and economic growth variables have a positive and significant effect on life expectancy in the long run, while only the economic growth variables have a significant effect on life expectancy in the short run. Therefore, the country needs to allocate greater expenditure on economic development because it can increase Gross Domestic Product (GDP) and reduce the inflation rate. Besides, people can have extra money to get better health. [ABSTRACT FROM AUTHOR]

As customers increasingly shift to online banking services, retail banks constantly review their retail banking network strategies to keep abreast with the trend. The paper studies data mining (DM) classification to determine the branch status of a case study involving 242 retail bank branches in Malaysia and 74 attributes of these branches. The likelihood of a branch being open or close soon was rated in an expert survey. The value is converted into a variable termed as aggregate closure possibility and discretized differently into the target classes of two balanced and unbalanced datasets. In the unbalanced dataset, attribute selection was applied only on instances randomly extracted from the majority classes. Then, two experiments were carried out to identify the best performing classifiers into these datasets. The results show that the decision table classifier produces superior performance over other classifiers. Attribute selection and instance reduction entailed more efficient datasets for data mining. Despite the mildly affected overall classification accuracy, the results are acceptable as there was no impact on the prediction of the critical class (To_Close). The study demonstrates the DM technology to emulate expert decision-making in the retail banking sector. [ABSTRACT FROM AUTHOR]