Predictive Analysis of Tuberculosis Treatment Outcomes Using Machine
  Learning: A Karnataka TB Data Study at a Scale
SeshaSai Nath Chinagudaba Darshan Gera Krishna Kiran Vamsi Dasu Uma Shankar S Kiran K Anil Singarajpure Shivayogappa. U Somashekar N Vineet Kumar Chadda Sharath B N
Tuberculosis (TB) remains a global health threat, ranking among the leading
causes of mortality worldwide. In this context, machine learning (ML) has
emerged as a transformative force, providing innovative solutions to the
complexities associated with TB treatment.This study explores how machine
learning, especially with tabular data, can be used to predict Tuberculosis
(TB) treatment outcomes more accurately. It transforms this prediction task
into a binary classification problem, generating risk scores from patient data
sourced from NIKSHAY, India's national TB control program, which includes over
500,000 patient records.
  Data preprocessing is a critical component of the study, and the model
achieved an recall of 98% and an AUC-ROC score of 0.95 on the validation set,
which includes 20,000 patient records.We also explore the use of Natural
Language Processing (NLP) for improved model learning. Our results,
corroborated by various metrics and ablation studies, validate the
effectiveness of our approach. The study concludes by discussing the potential
ramifications of our research on TB eradication efforts and proposing potential
avenues for future work. This study marks a significant stride in the battle
against TB, showcasing the potential of machine learning in healthcare.