"Non-Invasive-Cancer-Detection-Using-Breath-Volatile-Organic-Compounds-and-Support-Vector-Machine"

"This dataset provides a synthetic collection of breath-based volatile organic compound (VOC) measurements for the non-invasive detection of cancer using machine learning techniques. The data were generated based on reported concentration ranges of clinically relevant VOC biomarkers, including acetone, isoprene, ethanol, formaldehyde, benzene, toluene, and methane, which are known to be associated with metabolic alterations in cancer patients. In addition to VOC features, demographic variables such as age, sex, and smoking status are included to enhance classification performance.The dataset consists of 1,000 samples, balanced between healthy individuals and cancer cases, and is formatted in CSV to ensure compatibility with common data analysis frameworks. A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is employed as the baseline classification model, demonstrating the suitability of the dataset for supervised learning, feature analysis, and performance evaluation using standard biomedical metrics.This dataset is intended for research and educational purposes, particularly in the areas of non-invasive cancer diagnosis, breath analysis, gas sensor data processing, and machine learning model development. It can be used to benchmark classification algorithms, evaluate feature relevance, and support the design of breath-based diagnostic systems."