A Machine Learning Approach to Clinical Diagnosis of Typhoid Fever

Abstract

Typhoid fever is one of the major life threatning diseases, accounting for the death of millions of people every year apart from contributing to economic backwardness, mostly in Africa. Prompt and accurate diagnosis is a major key in the medical field, the large number of deaths associated with typhoid fever is as a result of many factors which include: poor diagnosis, self medication, shortage of medical experts and insufficient health institutions. These prompted for the development of a typhoid diagnosis system that can be used by anyone of average intelligence as this will assist in quick diagnosis of the disease despite shortage of health institutions and medical experts. A machine learning technique was used on the labelled set of typhoid fever conditional variables to generate explainable rules for the diagnosis of typhoid fever. The labelled database was divided into five different levels of severity of typhoid fever and the classification accuracies on both the training set and testing set are 95% and 96% respectively. Implementation was carried out using Visual Basic as front end and MySQL as backend.