ISSN: 2582 - 9734
Vimmi Kochher, Dr. Shivani Sharma
CrossRef DOI URL : https://doi.org/10.31426/ijesti.2023.3.12.3951
Cancer remains one of the leading causes of mortality worldwide, necessitating advanced techniques for early detection and accurate diagnosis. Traditional diagnostic methods are often time-consuming and expensive. This study explores the application of data mining techniques in cancer prediction, utilizing machine learning algorithms such as decision trees, support vector machines (SVM), artificial neural networks (ANN), and clustering approaches. Through analysing large medical datasets, these techniques help in identifying high-risk individuals and improving diagnostic precision. The findings suggest that data mining significantly enhances early detection, treatment planning, and survival prediction. Despite challenges like data privacy and class imbalances, advancements in artificial intelligence continue to refine these predictive models, making them more reliable and accurate. .
2025
31
