Cancer Prediction in Healthcare: Leveraging Data Mining Techniques

Abstract

Data mining methodologies have been widely applied in healthcare research and innovation, and have been proven effective in supporting clinical analysis and predictions. Cancer remains one of the leading causes of mortality worldwide, showing a significant global health challenge. While cancer research has traditionally focused on medical and biological studies, data-driven approaches have emerged as an important complement to conventional methods. Cancer outcome prediction represents a complex and demanding task in healthcare analytics. Data mining methods enable the analysis of cancer incidence across demographic variables such as gender and geographic regions, as well as lifestyle and socioeconomic factors, including diet, education, marital status, and living conditions. These factors contribute significantly to cancer pattern recognition and risk assessment. Many existing predictive systems depend on expert-driven models that achieve high accuracy, but those are computationally intensive and time-consuming. In this research, experimental results indicate that techniques such as clustering and classification can provide high precision; however, optimizing computational efficiency remains a key research challenge. Consequently, the development of accurate and scalable data mining models is essential for effective cancer prediction and decision support.