Article type :

1

Author :

Faizan Nazi, Tauseef Abbas

Volume :

2

Issue :

2

Abstract :

Abstract Precision medicine has emerged as a promising approach in cancer treatment, aiming to tailor therapies to individual patients based on their unique molecular profiles. Central to this endeavor is the accurate classification of cancer subtypes, which enables targeted interventions for improved outcomes. Machine learning (ML) techniques have revolutionized cancer research by offering sophisticated tools to analyze complex datasets and uncover patterns that may elude traditional methods. In this study, we explore the application of ML algorithms for cancer subtype classification and its implications for precision medicine. Leveraging large-scale genomic, transcriptomic, and clinical data, we demonstrate the potential of ML models to stratify cancer patients into distinct subgroups with varying prognostic and therapeutic implications. Through comprehensive feature selection and model optimization, our approach achieves high accuracy in identifying diverse cancer subtypes across multiple cancer types. Moreover, we showcase the integration of ML-based subtype classification into clinical practice, facilitating personalized treatment strategies and guiding therapeutic decisions. Challenges such as data heterogeneity, model interpretability, and clinical validation are discussed, along with potential strategies to address them. Overall, our findings underscore the transformative role of ML in advancing precision medicine for cancer patients, offering new avenues for tailored therapies and improved clinical outcomes.