Organizational inefficiency, infrastructural decay and the challenge of untrained ICT personnel in the health sector have all contributed to the erratic development in healthcare delivery in the Niger Delta region. The region generates the nation's crude-oil, yet is marginalized, resulting in the neglect of infrastructural development and the collapse of functional health systems. Humans now suffer from various kinds of diseases resulting from the effects of oil exploitation and exploration, and gas flaring. This study is focused on growth within the health sector, identifying the extent of ICT application, the application of appropriate ICT policy in the sector, and the influence of related policy. Critical data were obtained from key-players by way of interviews and questionnaires. A quantitative analysis was carried out on the data obtained at an organizational level. Several tests were performed on the parameters; descriptive statistics, and where necessary, the Pearson correlation coefficient were introduced to check relationships between variables. Results obtained indicated low-level adoption rates of ICT application in healthcare delivery and a need for an enabling policy. This study confirmed the low levels of healthcare delivery in the region and the importance of an ICT policy in the healthcare sector to improve efficiency.

HIV/AIDS has gained popularity and sufficient research time in the last two centuries. Research has shown that it is most predominant in people between the ages of 15-50. A lot of government and nongovernment organizations have been actively involved in finding ways to help monitor and curb the spread of the disease. Hitherto, there is no clear relevant predictive service available to HIV/AIDS control and research agencies. In this paper, the artificial neural network (ANN) is used in the prediction of prevalence and spread of HIV/AIDS. Results from a detailed analysis of a sample data used prove the robustness of the method.

Background: Policy formulation and implementation are important aspects of the policy process that have attracted the attention of several researchers. From these studies, it has been determined that a reliable policy system is needed for progress and growth in any sector. Methods: A robust theoretical or conceptual framework is designed in this paper to enable a seamless policy formulation and implementation through the implementation of a variant of the Kingdon’s Multiple Streams Framework. The framework designed in this study employs various building blocks needed to form a reliable policy for any sector in order to facilitate growth and development. The conceptual framework (building blocks) serves as a lens through which the data collected for the study is analysed, based on the constructs upon which the study is conducted. To apply the proposed model, some hospitals in the Niger Delta were visited and served questionnaire. The questionnaire focused on the level of infrastructural development and the introduction of information and communication technology(ICT) e-health solution for healthcare delivery. Results: Statistical results from the questionnaire were obtained and used to address organizational, infrastructural and individual challenges in relation to policy formulation and implementation within the sector. The overall result proves the robustness of the proposed model. Conclusion: The paper presents a detailed and painstaking examination of the various aspects of the Kingdon’s model, and its application in the proposed model for policy formulation and implementation. The proposed framework is adopted in the study of Nigeria’s healthcare sector with a case study on the Niger Delta.

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Purpose -The K-means clustering algorithm has been intensely researched owing to its simplicity of implementation and usefulness in the clustering task. However, there have also been criticisms on its performance, in particular, for demanding the value of K before the actual clustering task. It is evident from previous researches that providing the number of clusters a priori does not in any way assist in the production of good quality clusters. The authors' investigations in this paper also confirm this finding. The purpose of this paper is to investigate further, the usefulness of the K-means clustering in the clustering of high and multi-dimensional data by applying it to biological sequence data. Design/methodology/approach -The authors suggest a scheme which maps the high dimensional data into low dimensions, then show that the K-means algorithm with pre-processor produces good quality, compact and well-separated clusters of the biological data mapped in low dimensions. For the purpose of clustering, a character-to-numeric conversion was conducted to transform the nucleic/amino acids symbols to numeric values. Findings -A preprocessing technique has been suggested. Originality/value -Conceptually this is a new paper with new results.