Synthetic Tuberculosis Symptom Dataset

Dataset Features

• fever_for_two_weeks: Presence of fever lasting two weeks (Yes/No).
• coughing_blood: Occurrence of coughing up blood (Yes/No).
• sputum_mixed_with_blood: Presence of blood in sputum (Yes/No).
• night_sweats: Experiencing excessive sweating at night (Yes/No).
• chest_pain: Presence of chest pain (Yes/No).
• back_pain_in_certain_parts: Experiencing localized back pain (Yes/No).
• shortness_of_breath: Difficulty in breathing (Yes/No).
• weight_loss: Significant unexplained weight loss (Yes/No).
• body_feels_tired: Experiencing fatigue and tiredness (Yes/No).
• lumps_armpits_neck: Appearance of lumps around the armpits and neck (Yes/No).
• cough_and_phlegm_two_to_four_weeks: Persistent cough and phlegm for two to four weeks (Yes/No).
• swollen_lymph_nodes: Presence of swollen lymph nodes (Yes/No).
• loss_of_appetite: Decreased appetite (Yes/No).
• Prediction: Tuberculosis diagnosis prediction (Yes/No).

Distribution

Usage

• Tuberculosis Risk Prediction: Build models to predict tuberculosis status based on symptom patterns.
• Health Behavior Analysis: Study the impact of symptoms and their correlation with tuberculosis.
• Medical Research: Explore associations between common symptoms and tuberculosis diagnosis.
• Preventative Healthcare: Identify key predictors to inform early detection and prevention strategies for tuberculosis.
• Policy and Decision Making: Provide insights into population health trends and guide public health initiatives.

Coverage

License

Who Can Use It

• Data Scientists and Machine Learning Practitioners: For classification tasks related to tuberculosis prediction and risk analysis.
• Healthcare Researchers: To study risk factors and prevalence of tuberculosis in a synthetic population.
• Public Health Professionals: For insights into population health trends and preventative care.
• Educators and Students: As a teaching resource for analyzing health-related datasets and building predictive models.