Comparative Analysis of the C4.5 and ID3 Decision Tree Algorithms for Disease Symptom Classification and Diagnosis

Most disease surveillance outfits and authrorities around the world battle with one key challenge – the useful and objective handling and processing of the huge sets of disease data being generated on a regular basis as their personnel exercise their disease surveillance mandate. Many theories have been put forth on how best this could be tackled. Among these, is the use of information technology and mathematical theories and concepts to alleviate the problem. One of the most solid and promising methods includes the use of artificial intelligence techniques to help break down and make good sense of the data sets. This research looks to compare the usage of the C4.5 and the ID3 decision tree theory concepts as means of tackling making the best of disease surveillance data. The C4.5 and ID3 algorithms provide a method of breaking down the data and generating (among other useful information) the entropies and information gains of some predefined variables from huge sets of disease outbreak data. Once the information gain scores for the variables are computed, they can be easily ranked to determine the variable to define the root node in the decision tree, as the rest of the variables follow through as leaf nodes. Notably, there will be two sets of entropies and information gains; one from the C4.5 algortihm and the other from the ID3 algorithm. Both decision trees shall have validation steps after each branch pass to determine whether it is time to stop growing it or not. This is one of the mechanisms employed here to avoid overfiting of the decision tree (especially for the ID3 algorithm)

Lessons learnt from the 2014 West Africa ebola viral disease (EVD) outbreak: economic, political and social impacts of disease outbreaks

In many disease outbreaks, their effects can invariably be measured in both direct and indirect terms; directly by observable, measurable outcomes and indirectly by looking for knock-on effects post the disease outbreak. Some of these angles and degrees of measure could matter more and provide a different yet more objective measure of a true disease outbreak’s impact. Such measures include the economic, social and political implications following a disease outbreak. This research looks to study and document the economic, social and political implications of the 2014 West African EVD outbreak that mainly ravaged Guinea, Liberia and Sierra Leone. Whilst the outbreak may have been theoretically localized around the three countries, other neighboring and far flung but somewhat affiliated nations also had their share of the outbreak’s implications. This research also looks to study and identify knock-on effects of the outbreak in the other countries (outside the three at the outbreak’s epicenter). The research looks to inform and boost the focus on early and targeted mitigation efforts if only to safeguard the interests of regional blocks and other nations that may be victims of negative downturns as a result of such disease outbreaks. The research hopes to inform and spur intraregional and inter-national discussions and engagements on how to best deal with such disease outbreak in a measurable and sustainable manner, with an aim to possibly safeguard their socio-economic and political interests