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Typhoid fever, also called enteric fever, is caused by Salmonella enterica serovarTyphi, a gram-negative
bacterium. Estimates for 2000 suggest that around 21.5 million infections and 200 thousand fatalities due
to typhoid fever are reported worldwide every year. Typhoid fever and paratyphoid fever, especially
Among Nigerian kids and adolescents, keep being significant causes of disease and death. The aim of this
study is to use Techniques for Data Mining to develop a Typhoid risk predictive model in Nigerians using
relevant risk factors.
Historical data on the distribution of typhoid risk among participants were gathered using questionnaires
after medical professionals identified linked typhoid risk variables. The predictive model for typhoid risk
was developed using the algorithm for decision trees to define and account for variables linked to typhoid
risk The Waikato Environment for Knowledge Analysis (WEKA) software – a suite of machine learning
algorithms was used to develop the predictive model as the simulation environment. Holdout and 10-fold
cross-validation techniques were used to evaluate the performance of the algorithms. The data sets were
therefore subject to 10-fold cross validation using the two (2) chosen decision trees learning algorithms,
namely: C4.5 implemented as the WEKA J48 algorithm and the Naïve Bayes algorithm.
The result of the performance evaluation of the C4.5 and naïve Bayes’ algorithms are presented in Table 4.3.
The true positive rate which gave a description of the proportion of actual cases that was correctly predicted
which showed values of 0.783, 0.519, 0.722 and 0.619 respectively for no, low, moderate and high risk cases
by the C4.5 decision trees algorithm and 0.739, 0.556, 0.611 and 0.667 for the naïve Bayes classifier.
The study presented a predictive model of typhoid risk using relevant risk factors selected from a predefined
set of typhoid risk factors in Nigerians using the C4.5 decision trees algorithm that outperformed the
performance of the classification of the naïve Bayes. A stronger understanding of the connection between
the characteristics appropriate to typhoid risk was suggested following the creation of the forecast model
for typhoid risk classification. The model can also be incorporated into the current Health Information
System (HIS) that captures and manages clinical data that can be supplied to the predictive model of
typhoid risk classification, thus enhancing clinical choices influencing typhoid risk and evaluating clinical
data that affects typhoid risk from distant places in real time.