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Liver Disease Prediction Dataset
Patient Health Records & Digital Health
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About
This dataset illuminates factors that influence liver health, offering an opportunity to unveil patterns and predictors in liver well-being. Its primary purpose is to support predictive modelling and the development of healthcare strategies. It is particularly suitable for educational purposes in data science and machine learning projects, enabling users to focus on critical analysis aspects.
Columns
- Age: Represents the age of individuals, ranging from 20 to 80 years.
- Gender: A binary indicator where 0 denotes Male and 1 denotes Female.
- BMI (Body Mass Index): Indicates body mass, with values ranging from 15 to 40.
- Alcohol Consumption: Measures weekly alcohol intake, from 0 to 20 units per week.
- Smoking: A binary indicator where 0 signifies No smoking and 1 signifies Yes.
- Genetic Risk: Categorises genetic predisposition as Low (0), Medium (1), or High (2).
- Physical Activity: Records weekly physical activity in hours, ranging from 0 to 10.
- Diabetes: A binary indicator where 0 means No diabetes and 1 means Yes.
- Hypertension: A binary indicator where 0 means No hypertension and 1 means Yes.
- Liver Function Test: Provides a score for liver function, ranging from 20 to 100.
- Diagnosis: A binary outcome (0 or 1) indicating the presence or absence of liver disease.
Distribution
This dataset is provided in a CSV format, typical for tabular data, and is named 'Liver_disease_data.csv'. It contains 1700 records and comprises 11 distinct columns. The file size is 150.24 kB. Its structure includes features that span demographic, lifestyle, and various health indicators, making it well-suited for binary classification tasks.
Usage
This dataset is ideal for a range of applications, including:
- Research Insights: Exploring correlations between lifestyle/health factors and liver disease, and developing predictive models.
- Healthcare Interventions: Informing preventive measures and personalising healthcare strategies based on identified risk factors.
- Data Science and Machine Learning Projects: A suitable resource for developing and fine-tuning predictive models related to liver health.
Coverage
The dataset focuses on demographic indicators such as age (20-80 years) and gender (Male/Female). It also covers various lifestyle and health indicators relevant to liver health. This is a synthetic dataset generated for educational purposes. It does not include geographic or specific time range information, and it's noted that additional external factors impacting liver health are not incorporated.
License
the Attribution 4.0 International (CC BY 4.0)
Who Can Use It
This dataset is intended for:
- Researchers: Those seeking to explore correlations and build predictive models for liver disease.
- Healthcare Professionals: Individuals interested in informing preventive measures and personalising healthcare.
- Data Scientists and Machine Learning Practitioners: Anyone looking for a dataset to develop and fine-tune machine learning models, especially for binary classification problems.
Dataset Name Suggestions
- Liver Disease Prediction Dataset
- Synthetic Liver Health Predictor
- Healthcare Risk Factors Dataset
- Predictive Liver Health Indicators
- Machine Learning Liver Disease Data
Attributes
Original Data Source: Liver Disease Prediction Dataset
QUALITY