Background: Tuberculosis is the largest killer among communicable diseases in the 15 to 49 years age group, when humans are most productive. In 2021, there were an estimated 10 million new TB cases worldwide. One of the targets of the Sustainable Development Goals (SDGs) for 2030, is to end the global tuberculosis (TB) epidemic. Failure to properly treat drug susceptible tuberculosis (DSTB) can leads to the acquisition of multi drug resistance TB (MDRTB). The cost per patient treated is usually in the range of US$100 to 1000 for DSTB and US$2000 to 20000 for MDRTB. Meaning for example that, in addition to the US$2.66 to 26.57 million needed for the annual treatment of DSTB patients in Cameroon, it will cost US$0.75 to 7.44 million to treat the estimated 1.4% MDRTB patients that could result from the poor outcome treatment. Preventing treatment failures of DSTB is therefore both a health and financial issue. The decision support tool, which can measure the risk of unsuccessful treatment outcome, very popular and worldwide used in the domain of finance, is not yet available or popularized to prevent a favourite treatment outcome of infectious diseases such as tuberculosis. The objectives of this study were to use the Statistical method of Scoring to identify factors associated with the tuberculosis unsuccessful treatment outcome and to predict the treatment outcome.