This dataset contains diagnostic test results for predicting health conditions based on nine key biomarkers, including blood glucose, HbA1C, blood pressure, cholesterol levels, and hemoglobin. It helps develop AI models for disease risk assessment, preventive healthcare, and insurance underwriting by classifying individuals into five conditions: Fit, Anemia, Hypertension, Diabetes, and High Cholesterol.
Healthcare analytics is evolving with AI and data-driven insights, enabling early detection of diseases and personalized treatment recommendations. This dataset provides real-world diagnostic test results of individuals, focusing on nine key medical parameters. The goal is to predict potential health conditions based on these test values, making this dataset highly relevant for medical research and diagnostics.
The dataset is structured to help machine learning practitioners, healthcare professionals, and data scientists develop predictive models for common health conditions such as diabetes, anemia, hypertension, and high cholesterol. By analyzing patterns in diagnostic values, this dataset can be leveraged for:
• Health risk scoring and prediction
• Preventive healthcare research
• Anomaly detection in medical test results
With the increasing adoption of AI in healthcare, this dataset serves as a valuable resource for developing classification models that assist in risk assessment and disease prediction.
Dataset Features:
• Independent Variables (Medical Test Results):
o Blood Glucose – Measures blood sugar levels.
o HbA1C – Indicator of long-term blood sugar levels.
o Systolic BP – Measures the top value of blood pressure.
o Diastolic BP – Measures the bottom value of blood pressure.
o LDL – Low-density lipoprotein (bad cholesterol).
o HDL – High-density lipoprotein (good cholesterol).
o Triglycerides – A type of fat found in the blood.
o Haemoglobin – Measures red blood cell oxygen-carrying capacity.
o MCV (Mean Corpuscular Volume) – Measures average red blood cell size.
• Target Variable(Health Condition Prediction):
o Fit – No significant health issues detected.
o Anemia – Low haemoglobin or red blood cell count.
o Hypertension – High blood pressure condition.
o Diabetes – High blood glucose and HbA1C levels.
o High Cholesterol – Elevated LDL and triglycerides.
Applications & Use Cases:
o Health Risk Prediction – Use ML models to predict disease likelihood.
o Medical Decision Support – Aid healthcare providers in diagnosing conditions.
o Data Science in Healthcare – Experiment with classification models.
Potential Machine Learning Approaches:
• Classification Algorithms: Decision Trees, Random Forest, SVM, Naïve Bayes
• Feature Engineering: Normalize numerical features for better model performance
• Model Evaluation: Accuracy, Precision-Recall, F1 Score for medical predictions
This dataset is ideal for AI-powered healthcare applications and insurance risk assessment models.