The use of Machine Learning in Predicting Disease Progression

Abstract

Machine learning (ML) has emerged as a powerful tool in the healthcare sector, especially in predicting disease progression. This paper examines the application of various ML algorithms, including supervised, unsupervised, and reinforcement learning, in predicting disease trajectories, particularly in complex and chronic diseases like Alzheimer’s, multiple sclerosis, cardiovascular disease, and diabetes. By leveraging clinical data, genetic information, and patient history, ML models like random forests and neural networks can accurately predict the time to disease progression. This has profound implications for early diagnosis, personalized treatment, and patient management. However, the integration of these models into clinical practice faces challenges related to data quality, interpretability, and deployment in real-world settings. Despite these limitations, the case studies reviewed demonstrate the transformative potential of machine learning in enhancing decision-making processes in healthcare. Keywords: Machine learning, Disease progression prediction, Healthcare, Supervised learning, Chronic diseases.