Abstract:
Typhoid fever is a serious and potentially fatal illness that causes a large number of
deaths each year, particularly in developing countries. Early and accurate diagnosis
is essential for effective treatment and to prevent the spread of the disease. In this
study, we used machine learning techniques to develop a predictive model for typhoid
fever. We used a dataset of 1746 entries and 29 attributes, and applied ten different
algorithms to the data. Our results showed that machine learning can be effective tools
for the prediction of typhoid fever, with the XGBoost classifier performing
particularly well, achieving an accuracy rate of 97.87%. In addition to the XGBoost
classifier, we also evaluated the performance of several other algorithms, including
the Random Forest classifier, Extra Trees classifier, and Artificial Neural Network.
While each of these classifiers performed well, the XGBoost classifier was found to
be the most effective, with accuracy rates of 97.78% and 97.42% for the Random
Forest and Extra Trees classifiers, respectively. Overall, our results demonstrate the
potential of machine learning and ANNs for the prediction of typhoid fever, and
suggest that these technologies could be useful tools for improving the diagnosis and
treatment of this disease. Further research will be needed to explore the potential of
these technologies in more detail, and to identify the most effective approaches for
their implementation and deployment.
