Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart disease diagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostic tools and shortage of medical professionals and other resources which affect proper prediction and treatment of patients. Inadequate preventive measures, lack of experienced or unskilled medical professionals in the field are the leading contributing factors. Although, large proportion of heart diseases is preventable but they continue to rise mainly because preventive measures are inadequate. In today's digital world, several clinical decision support systems on heart disease prediction have been developed by different scholars to simplify and ensure efficient diagnosis. This paper investigates the state of the art of various clinical decision support systems for heart disease prediction, proposed by various researchers using data mining and machine learning techniques. Classification algorithms such as the Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Network (ANN) have been widely employed to predict heart diseases, where various accuracies were obtained. Hence, only a marginal success is achieved in the creation of such predictive models for heart disease patients therefore, there is need for more complex models that incorporate multiple geographically diverse data sources to increase the accuracy of predicting the early onset of the disease.
This paper presents a review on reducing corruption in African developing countries, to lessen the discretion of officials, and increase transparency. While it is true that ICT eliminates many opportunities for corruption for those who do not understand the new technology fully, however, it opens up new corruption vistas for those who understand the new systems well enough to manipulate them. Therefore proper safeguards are needed. Putting in place systemic hurdles may prevent people from abusing their power for private gain. While complete eradication of corruption is difficult to achieve, much can be done in reducing its prevalence. ICT can support actors wishing to improve governance capacity and fight corruption, but the surrounding political, social and infrastructural environment will decide if the technology is to be used to its fullest potentials. Automating existing bureaucratic processes that are defective will not yield good results. In this paper, we propose a methodology to combat corruption using information and communication technologies (ICT) that entails process restructuring. Most developing countries are not fully ready to embrace a comprehensive program of e-government, thus transparency is not holistic in all the sectors. Rather than wait for total readiness, an approach of learning by trial and consolidating small gains are recommended. While e-Governance holds great promise in many developing countries however, substantial challenges are to be tackled. Many ICT projects fail because of insufficient planning capacity and political instability.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.