LEVERAGING MACHINE LEARNING FOR PREDICTIVE ANALYTICS IN CANCER PROGNOSIS AND TREATMENT OPTIMIZATION

Abstract

The medical community recognizes cancer as a multiple-disorder condition with distinct groupings. Cancer research now depends heavily on early cancer type identification and prognosis because these developments improve the subsequent therapeutic treatments for patients. Multiple research groups in biomedical and bioinformatics fields use machine learning (ML) techniques to study patient risk classification following the recognition that healthcare needs to separate subjects into high and low-risk categories. The development of neoplastic disease therapeutic approaches along with cancer development prediction utilizes these scientific methods. Computer algorithms demonstrate vital importance because they can find essential characteristics within complex datasets. Few widely applied approaches include Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), and Decision Trees (DTs which researchers use in cancer research to build predictive models for both effective and precise decision support systems. The integration of machine learning technologies in daily clinical practice requires thorough confirmation before they can be implemented due to their potential benefits in cancer development understanding.  A study of present-day machine learning models that model cancer development is outlined throughout this research. This analysis contains supervised machine learning algorithms which have separate input requirements and data sets for prediction models.  The increase in machine learning applications in cancer research makes it essential to analyze recently published studies that use these methods to forecast cancer chances and patient results.