Sanjay Satya-Akunuri Koka, Rohith Suba Koka, Purushotham Rudraraju, Kiran Kumar
Abstract: Mycobacterium Tuberculosis, TB, is one of the deadliest diseases in the world, it causes over 1.3 million deaths per year. There has been constant need for innovative research methods on a clinical basis to combat the spread of TB and ultimately lead to the eradication the disease all over the world, these efforts have proved futile as the disease is still active. Therefore, it is imperative to study TB through the uses of Big Data Analytics and precision medicine/predictive intelligence. This project utilized vast amounts of public domain data from the World Health Organization to organize past data and build accurate predictive models based on various key performance indicators to determine the incident rate of TB from the years 2000 to 2030. The project resulted in discovery that TB incident rates in the world would still occur at a high rate in 2030. Due to the amount of data available, it was possible to organize, refine, and implement previous data sets in order to examine trends that may lead further action taken in order to incorporate a new public health model for TB-vulnerable countries such as India, China, and Indonesia.
Keywords: Big Data, Tuberculosis, Precision Medicine, Public Health