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Synthetic Prostate Cancer Detection Dataset for AI Training
Clinical Trials & Research
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About
The Synthetic Prostate Cancer Dataset has been generated for educational and research purposes to support the analysis of morphological and textural attributes associated with prostate cancer diagnosis. This synthetic, anonymised dataset enables exploration of clinical and imaging-like features often used in medical decision-making and cancer detection.
Dataset Features
- ID: Unique identifier for each patient record.
- Diagnosis Result: Indicates whether the tumor is malignant (M) or benign (B).
- Radius: Measure of the radius of the cell nuclei.
- Texture: Standard deviation of grayscale values, representing texture variation.
- Perimeter: The perimeter of the tumor region.
- Area: The area enclosed by the tumor perimeter.
- Smoothness: Local variation in radius lengths.
- Compactness: Perimeter² / Area - 1.0, describing shape compactness.
- Symmetry: Symmetry of the tumor shape.
- Fractal Dimension: Measure of tumor boundary complexity.
Distribution
Usage
This dataset can be used for the following applications:
- Cancer Research: Explore patterns and differences in tumour morphology related to benign and malignant cases.
- Predictive Modelling: Train and test machine learning algorithms for binary classification of cancer status.
- Clinical Insight: Analyse how combinations of geometrical and textural features correlate with diagnosis outcomes.
- Educational Purposes: Provide students and researchers with practical experience in medical data analysis and model development.
Coverage
The dataset is synthetically generated and fully anonymised. It includes both numeric and categorical data types, supporting a wide range of supervised learning and exploratory analysis techniques in medical informatics and cancer detection research.
License
CC0 (Public Domain)
Who Can Use It
- Medical Researchers and Oncologists: To examine tumor characteristics and evaluate diagnostic indicators.
- Data Scientists: To develop and benchmark machine learning models for cancer detection.
- Healthcare Educators and Students: As a robust resource for learning about biomedical data processing and analysis.
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