Machine Learning Algorithms for Early Disease Detection: A Computational STEM Perspective

Resumen

Early disease detection is a pivotal challenge in modern healthcare, where timely diagnosis can significantly enhance treatment outcomes and reduce mortality. With the exponential growth of medical data, machine learning (ML) has emerged as a transformative tool in biomedical informatics, enabling the identification of subtle patterns and predictive biomarkers that elude traditional diagnostic methods. This paper explores the application of various ML algorithms—such as support vector machines (SVM), random forests, artificial neural networks (ANN), and ensemble learning techniques—for the early detection of diseases including cancer, diabetes, cardiovascular disorders, and neurodegenerative conditions. Emphasis is placed on the integration of structured (e.g., electronic health records) and unstructured data (e.g., medical imaging, genomics), along with the implementation of deep learning models for complex feature extraction. We discuss key challenges in data preprocessing, class imbalance, interpretability, and model generalization, which are critical for clinical translation. Additionally, the importance of cross-disciplinary collaboration among computer scientists, clinicians, and bioinformaticians is underscored to ensure the development of robust and ethically responsible models. Case studies highlight successful deployments of ML in early breast cancer detection using mammograms, prediction of diabetic retinopathy through retinal imaging, and early Alzheimer’s diagnosis via neuroimaging analysis. Future directions include the use of federated learning for privacy-preserving model training, reinforcement learning for personalized screening strategies, and explainable AI for increased transparency. The convergence of computational STEM methodologies with ML offers a promising avenue for revolutionizing preventive medicine through data-driven insights and real-time decision support systems.