Predictive Analysis of Tuberculosis Treatment Outcomes Using Machine Learning: A Karnataka TB Data Study at a Scale

TL;DR

This study demonstrates how machine learning models, trained on large-scale Indian TB patient data, can accurately predict treatment outcomes, potentially aiding TB eradication efforts.

Contribution

It introduces a novel ML approach using tabular data and NLP techniques to predict TB treatment outcomes with high accuracy on a large dataset.

Findings

Achieved 98% recall and 0.95 AUC-ROC on validation data.

Validated the effectiveness of ML and NLP in TB outcome prediction.

Discussed implications for TB eradication strategies.

Abstract

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.