Unlocking Cancer Prevention In The Era Of Ai: Machine Learning Models For Risk Stratification And Personalized Intervention

Abstract

This paper aims to discuss the evolution of machine learning based approaches for identifying higher risk individuals. It is relevant preventive measures with the reference to the individual characteristics of patients. It is big data that include genetic, environmental, and lifestyle data, these models improve the accuracy of risk prediction and identify protective strategies on the individual level. A paradigm shift in the cancer prevention strategies has occurred owing to the incorporation of Artificial Intelligence and machine learning in the modern world. The role of machine learning in early recognition of high-risk personalities of contracting cancer is examined to support malt measures. It overviews the available literature regarding the ethical dilemma, direction and obstacle on the ordeal of integrating AI guided targeted treatment in clinical setting. The results set the stage for exploring the possibilities of big data and AI for cancer prevention, which could eventually result in equitable benefits to the quality of individual patients’ care and expenses for the entire healthcare system. Cancer is among the most prevalent diseases resulting in morbidity and mortality of patients globally. The recent advancements in diagnostics, evaluation of the cancer prognosis and primary treatment of the patients, data-personalized therapy has not been systematically addressed. Artificial Intelligence deployed to forecast and orchestrate many cancers, has being seen as a talented instrument for enhancing healthcare predicates and patients. The AI applications in the field of Oncology are risk evaluation, diagnosis, prognosis and decision making in treatments with precise and accurate knowledge. Artificial intelligence, a broad category of which a segment involves machine learning which involves using past data to develop a solution to solve a problem has been able to predict most forms of cancer such as breast, brain, lung, liver, and prostate among others. It is observing that both AI and ML exhibit a more favorable performance compared to clinicians in terms of cancer prediction. These technologies also have the competency to enhance the diagnostic and prognostic possibilities for numerous diseases including but not exclusive to cancer, as well as enhance the quality of life of the affected patient. Thus, further enhancement of the existing Artificial Intelligence and Machine learning schemes is essential. In conclusion, it is evident that Artificial Intelligence and Machine learning strategies are interwoven, the overall health of the society. The incidence of the burden of cancer can be significantly enhanced with the provision of an experts’ proactive approach.