Impact of Machine Learning in Acute Myeloid Leukemia (AML) with Prognosis Approach for Better Accuracy

Abstract

Acute Myeloid Leukemia (AML) is a complex hematologic malignancy characterized by rapid progression and heterogeneity in patient outcomes. Prognostic assessment plays a crucial role in guiding treatment decisions and improving patient care. Traditional prognostic models in AML rely on clinical and genetic features, yet they often lack precision due to inherent complexities and dynamic disease behavior. This paper explores the transformative impact of machine learning (ML) techniques in enhancing AML prognosis accuracy. Leveraging large-scale datasets encompassing diverse clinical parameters, genetic mutations, and treatment responses, ML algorithms offer a promising avenue for personalized prognostication. Through the integration of advanced computational methods, such as deep learning, ensemble models, and feature selection techniques, ML frameworks can effectively discern subtle patterns and associations that evade conventional analyses. Furthermore, this study investigates the key challenges and opportunities in implementing ML-based prognostic models in clinical practice. Addressing issues related to data quality, interpretability, and model validation are paramount to ensuring robust and reliable prognostic predictions. Collaborative efforts between clinicians, researchers, and data scientists are essential for the successful translation of ML algorithms into actionable insights that inform therapeutic strategies and improve patient outcomes. Overall, the application of machine learning in AML prognosis represents a paradigm shift towards precision medicine, offering clinicians a powerful tool to navigate the complexities of disease heterogeneity and tailor treatment approaches to individual patient needs. As the field continues to evolve, continued research and innovation are crucial for realizing the full potential of ML-driven prognostication in AML management.