Tuberculosis Dataset for Intelligent and Adaptive Medical Diagnostic System

Tuberculosis is a communicable chronic disease and one of the top ten causes of death worldwide according to World Health Organization (WHO). With availability of clean and well encoded clinical data from tuberculosis patients, artificial intelligence and machine learning algorithms would be able to transform the management of tuberculosis patients through intelligent prediction and intervention. This dataset contains four hundred and thirty (430) clinical data from patients with tuberculosis at Tuberculosis and Leprosy Hospital, Eku, Delta State, Nigeria. The dataset was gathered through validated and structured questionnaire administered using random sampling after obtaining the patients' consent. The collated dataset was pre-processed and encoded with variables (features) for prediction which include cough, night sweat, breathing difficulty, fever, chest pain, sputum, immune suppression, loss of pleasure, chill, lack of concentration, irritation, loss of appetite, loss of energy, lymph node enlargement, systolic blood pressure and BMI. Prediction of tuberculosis based on the clinical data from patients' features would play an essential role in diagnosis, intervention and management of tuberculosis patient.