Cancer Detection Using Multifactorial Analysis for Examining the Impact of Lifestyle

Abstract

The increasing worldwide worry about cancer makes us need an ingenious prediction model for early diagnosis. In the realm of cancer prediction, the escalating global concern demands innovative solutions for early diagnosis. This paper addresses the pressing need by introducing a modern approach grounded in multimodal analysis. It includes various elements that affect lifestyles, genetics, and behavioral distinctions to improve people’s health and well-being, such as diet, height, weight, blood groups, marital status, smoking, and alcohol consumption. Our model based on the above-mentioned factors predicts cancer risk and offers a complete and well-timed assessment. Unlike traditional models, our pioneering method goes beyond singular indicators, offering a holistic prediction framework. This novel approach envisions a paradigm shift where individuals and healthcare professionals proactively assess and manage cancer risks. By leveraging AI and machine learning, our research propels the development of user-centered, comprehensive predictive models, promising transformative impact on public health and contributing to the evolution of healthcare systems.