Data is deemed as a gold mine if and only if it is analyzed and utilize. The healthcare system is one of the largest generators of data due to strict adherence to regulatory structure. Unfortunately, Big Data deployment in the healthcare industry has not catch up in Ghana and Africa at large. Big Data in healthcare has enormous benefits including the designing of Predictive models, analyzing disease patterns and tracking disease outbreaks, turning large data into actionable information, Evidence based health delivery through data analysis and Capture and analyze real time data from variety of locations. To achieve above mentioned potentials of Big Data, this thesis has taken a look at the structure of Big Data, which has led to the development of an architectural framework that will fit into the system Ghanaian healthcare system and how the variety of data will be handled and stored. A framework which will serve as a platform for data analytics in the Healthcare industry is also proposed. Finally, we propose a framework which will handle the new data generating devices used by health facilities that is the structured and unstructured data types.
Sentiment Analysis is a way of considering and grouping of opinions or views expressed in a text. In this age when social media technologies are generating vast amounts of data in the form of tweets, Facebook comments, blog posts, and Instagram comments, sentiment analysis of these usergenerated data provides very useful feedback. Since it is undisputable facts that twitter sentiment analysis has become an effective way in determining public sentiment about a certain topic product or issue. Thus, a lot of research have been ongoing in recent years to build efficient models for sentiment classification accuracy and precision. In this work, we analyse twitter data using support vector machine algorithm to classify tweets into positive, negative and neutral sentiments. This research try to find the relationship between feature hash bit size and the accuracy and precision of the model that is generated. We measure the effect of varying the feature has bit size on the accuracy and precision of the model. The research showed that as the feature hash bit size increases at a certain point the accuracy and precision value started decreasing with increase in the feature hash bit size.
Google map is a platform that gives visual representation of geographical locations on the planet earth. Google maps has many features that for displaying maps and adding external contents to the map. In recent years many institutions and organizations have customized the features and functions of Google maps to build new applications that address their specific needs. Developing nations are faced with booming population growth, inadequate infrastructure and services. To provide many important services, especially financial services it is required that people are accurately located by use of a verifiable address. Developing an effective addressing system has been a challenge for many developing nations due to inadequate road and street network. This paper discusses the use of Google maps API to link properties in Ghana, hence assigning a digital address to each landmark in the country. The paper examines the technology that Google maps API provides and how it was harnessed to develop the GhanaPostGPS addressing system. This application helps users to acquire their digital address and enables others to search the location of an address via the system. The result from the Google maps API reverse geocoding shows that there is no district in the JSON response, an indicating factor showing extra work done by the developers of the application. This clearly shows the work is not a direct replica of services offered by Google maps although some services of Google maps were employed.
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