Authors: Selva Murugan
Abstract: Chronic diseases such as diabetes, cardiovascular disorders, cancer, and neurodegenerative conditions represent a major global health burden. Early diagnosis and personalized treatment strategies significantly improve patient outcomes, and the identification of reliable biomarkers is central to these efforts. Machine learning (ML), a subset of artificial intelligence, has emerged as a powerful tool to analyze complex biomedical data and discover novel biomarkers that traditional statistical methods may overlook. This paper explores the application of machine learning techniques in identifying novel biomarkers for chronic diseases by integrating multi-omics data, clinical records, and imaging datasets. It discusses various ML algorithms, challenges in data preprocessing and interpretation, and the translational potential of ML-driven biomarker discovery for precision medicine.
DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.563
