Article type :

1

Author :

Youth Jeni, Maki Kali

Volume :

2

Issue :

2

Abstract :

Abstract Personalized cancer care has emerged as a promising approach to optimize treatment outcomes by tailoring therapies to individual patients. Central to this paradigm is the accurate stratification of patients into subgroups based on their unique molecular profiles and clinical characteristics. Machine learning techniques offer powerful tools for analyzing complex datasets and identifying patterns that can inform patient stratification in precision medicine. In this study, we propose a machine learning-driven approach for patient stratification in cancer care, with a focus on precision medicine. We leverage diverse data sources, including genomics, transcriptomics, proteomics, and clinical variables, to develop robust models that classify patients into clinically relevant subtypes. Our approach integrates feature selection methods to identify biomarkers and molecular signatures associated with different cancer subtypes, enhancing the interpretability and clinical relevance of the models. Through comprehensive evaluation using independent validation datasets, we demonstrate the effectiveness and generalizability of our machine learning models in accurately classifying patients and guiding treatment decisions. Furthermore, we discuss the potential implications of our approach for improving patient outcomes, optimizing resource allocation, and advancing personalized cancer care. Overall, our study highlights the promise of machine learning-driven patient stratification in enabling precision medicine approaches for cancer treatment.