Synthetic and Realistic Laser–Tissue Interaction Data for Machine-Learning Applications

This dataset provides two complementary CSV files containing simulated laser–tissue interaction measurements, designed to support machine-learning research in biomedical optics and computational modeling: 1- **laser_tissue_data.csv** - Samples: 1,000,000 Features: - wavelength (nm) - pulse_duration (ns) - energy_density (J/cm²) - absorption_coeff (cm⁻¹) - thermal_conductivity (W/(m·K)) - fluence (J/cm²/ns) – derived - beam_profile – simulated optical absorption -- success (0/1) – binary target indicating whether the laser pulse meets thermal diffusion and energy thresholds These data were generated via uniform, log-normal, and normal distributions to represent idealized measurements of laser–tissue interactions under controlled conditions. 2- **realistic_laser_tissue_data.csv** - Samples: 1,000,000 Same feature set as above, with two added realism factors: - Measurement noise (configurable noise_level = 0.1) applied to each physical variable - Label noise (flip_prob = 0.05) randomly inverting 5% of the success outcomes This version simulates sensor variance and occasional mislabeling, making it suitable for testing robustness of classification and regression models. ------------------------------------------------- Use Cases & Applications - Train and benchmark classification models (e.g., logistic regression, random forests, neural networks) on binary outcome prediction. - Explore feature-engineering strategies (e.g., interaction terms, normalization) for optical parameters. - Evaluate the impact of measurement and label noise on model performance and generalizability. - Develop synthetic-data augmentation pipelines for scarce or ethically constrained biomedical datasets.