Model for predicting drug resistance based on the clinical profile of tuberculosis patients using machine learning techniques.

Tuberculosis (TB) is a disease caused by the bacterium and despite effective treatments, still affects millions of people worldwide. The advent of new treatments has not eliminated the significant challenge of TB drug resistance. Repeated and inadequate exposure to drugs has led to the development of strains of the bacteria that are resistant to conventional treatments, making the eradication of the disease even more complex.

A study about management of drugs for leprosy patients under medical monitoring: A solution based on AHP-Electre decision-making methods.

Leprosy, also known as Hansen's, is one of the listed neglected tropical diseases as a major health problem global. Treatment is one of the main alternatives, however, the scarcity of medication and its poor distribution are important factors that have driven the spread of the disease, leading to irreversible and multi-resistant complications. This paper uses a distribution methodology to optimize medication administration, taking into account the most relevant attributes for the epidemiological profile of patients and the deficit in treatment via Polychemotherapy.