This study aims to quantify the extent of catastrophic household health expenditures on welfare and determine factors influencing it. A logistic regression model based on the logit link function was used to predict the probability of catastrophic health expenditure occurrence. A comparison between 2008 and 2012 health status of adults shows that there was a sizable improvement of the health status of individuals. The high level of catastrophic health expenditure may be associated with the low share of prepayment in national health expenditure, adequate availability of services and a high level of poverty which for South Africa is 46.2% according to the Statistics South Africa report (2015). Major factors determining the catastrophic expenditure besides poverty were spending on hospitalisation and medical supplies. Thus, reducing catastrophic expenditures requires an increase in financial protection offered to the poor through expanding government-financed benefits for the poor such as implementation of the Social Health Insurance (SHI) scheme, which will cover all poor households.

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The study placed a particular emphasis on the so called data mining algorithms, but focuses the bulk of attention on the C4.5 algorithm. Each educational institution, in general, aims to present a high quality of education. This depends upon predicting the students with poor results prior they entering in to final examination. Data mining techniques give many tasks that could be used to investigate the students' performance. The main objective of this paper is to build a classification model that can be used to improve the students' academic records in Faculty of Mathematical Science and Statistics. This model has been done using the C4.5 algorithm as it is a well-known, commonly used data mining technique. The importance of this study is that predicting student performance is useful in many different settings. Data from the previous students' academic records in the faculty have been used to illustrate the considered algorithm in order to build our classification model.

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Under-five mortality is among the major public health problems in developing countries, the rate of which is an important factor for a country’s development. For this reason, under-five mortality status is an important outcome to measure for children’s health. This study uses the Cox proportional-hazards model to identify risk factors associated with under-five mortality in Sudan. This study uses the 2014 Sudan Multiple Indicator Cluster Survey (MICS) conducted by the Central Bureau of Statistics in collaboration with several national institutions. The survival Cox proportional-hazards model was used to identify factors that affect under-five child mortality in Sudan. The results show that the weight of a child at birth is positively associated with the under-five child mortality rate. Under-five children who have both small and large weights at birth are at a higher risk of dying before reaching five years. Based on demographic factors associated with under-five mortality, our analysis showed that mothers who were married at the time of the survey are most likely to have higher under-five child mortality as compared to formerly married mothers. In addition to this, that mother’s age at the time of the birth is significantly associated with under-five mortality. Based on the result, the lack of important policies targeting the reduction of socioeconomic inequalities between rural and urban areas is the major problem of public health interventions to improve child health and survival in Sudan.