ARTIFICIAL INTELLIGENCE ANDMACHINE LEARNING APPROACHES FOREARLY CANCER DETECTION ANDPROGNOSIS

Cancer remains one of the leading causes of mortality worldwide, and its early detection plays a critical role in improving patient survival rates and treatment outcomes.Traditional diagnostic methods, while effective, often face limitations such as high cost, delayed detection, and dependency on expert evaluation.Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have opened new possibilities for enhancing cancer diagnosis, prediction, and prognosis.These technologies enable automated data analysis, uncover hidden patterns, and support clinical decisionmaking with higher accuracy and efficiency.This study explores various AI and ML approaches applied in early cancer detection and prognosis, focusing on supervised learning, deep learning, and ensemble techniques.The integration of algorithms with medical imaging, genomic data, and electronic health records has demonstrated remarkable improvements in identifying cancer at early stages.Deep learning models, particularly convolutional neural networks, have shown promising results in analyzing histopathological and radiological images.Similarly, machine learning algorithms such as Support Vector Machines, Random Forests, and Gradient Boosting have been effective in predicting cancer risk factors and survival rates.The abstract also highlights challenges associated with AI adoption in healthcare, including data privacy, model interpretability, and the need for large, high-quality datasets.Despite these challenges, AIdriven solutions hold immense potential to complement traditional diagnostic practices and advance personalized medicine.Future research should focus on explainable AI, robust validation frameworks, and collaborative systems that bridge the gap between data scientists and medical professionals.