Machine Learning-based Prediction Model for Cancer Detection

Cancer is a leading cause of death worldwide, and early detection is crucial for effective treatment. Machine learning (ML) algorithms have shown promising results in detecting cancer from clinical data and other biomarkers. This study presents a ML-based cancer detection model that analyzes patient data to predict the stages and types of cancer. The model uses supervised learning techniques including Decision Tree, Random Forest and XG Boost algorithm. The model has been trained on a large dataset of patient records for achieving high accuracy and sensitivity. The results demonstrate the potential of ML-based approaches for improving cancer detection and ultimately saving lives. This model has the potential to be integrated into clinical practice, aiding clinicians in making more informed decisions about patient care.