Detecting the Undetected: Machine Learning in Early Disease Diagnosis

• Published in: Basic & clinical pharmacology & toxicology Vol. 137; no. 4; pp. e70104 - n/a
• Main Authors: Rathi, Kanika, Sharma, Sakshi, Barnwal, Anil
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.10.2025
• Subjects: Algorithms; Artificial neural networks; Cardiovascular diseases; Cluster analysis; Clustering; Data integration; Decision trees; Deep Learning; Diagnosis; Disease; Disease detection; Early Diagnosis; explainable AI; Explainable artificial intelligence; Federated learning; Health care; healthcare; Humans; Infectious diseases; Learning algorithms; Machine Learning; Medical diagnosis; Neural networks; Neural Networks, Computer; Neurological diseases; Patients; Principal components analysis; Recurrent neural networks; Reinforcement; Support Vector Machine; Support vector machines; Unsupervised learning; early diagnosis; deep learning; explainable AI; machine learning; healthcare
• ISSN: 1742-7835; 1742-7843; 1742-7843
• DOI: 10.1111/bcpt.70104

ABSTRACT Early detection of diseases is a critical pillar in advancing modern healthcare, offering timely interventions and better patient outcomes. This overview highlights a range of machine learning (ML) approaches that are transforming early disease diagnosis. We discuss how traditional supervised and unsupervised methods, alongside advanced deep learning and reinforcement learning techniques, are utilized to detect early disease markers, often before clinical symptoms appear. The paper begins with a discussion of ML fundamentals within healthcare, along with standard evaluation metrics such as accuracy, precision, recall, F1‐score and AUC‐ROC. It then explores various ML models, including supervised algorithms (support vector machines, decision trees and random forests), unsupervised methods (K‐means, hierarchical clustering and principal component analysis) and deep learning architectures (convolutional neural networks, recurrent neural networks and transformers). Reinforcement learning's emerging role in healthcare is also examined. Practical applications across disease areas such as cancer, cardiovascular diseases, neurological disorders and infectious diseases are reviewed. We emphasize the importance of high‐quality datasets, balanced data distribution and clinical relevance. Key challenges such as data scarcity, model interpretability, privacy, the risk of overdiagnosis and clinical integration are critically discussed. It underscores that the successful translation of these technologies from code to clinic hinges on a deep, bidirectional collaboration between data scientists and clinical experts to ensure that newly developed tools address real‐world patient needs. The overview concludes with future directions, including explainable AI, federated learning, multimodal data fusion, real‐time applications and quantum ML, charting the evolving path of early disease detection. Summary Machine learning (ML) offers a transformative approach to early disease diagnosis by analysing complex healthcare data to detect subtle disease markers, often before clinical symptoms appear. This focused review outlines supervised, unsupervised, deep learning and reinforcement learning approaches and their applications across cancer, cardiovascular, neurological and infectious diseases. Successful clinical integration of ML models requires overcoming challenges such as data quality, bias, model interpretability, patient privacy and the risk of overdiagnosis. High‐quality, balanced and clinically validated datasets are essential for reliable diagnostic performance and safe deployment in real‐world healthcare. Strong collaboration between data scientists and clinicians is critical to ensure that ML‐driven diagnostic tools are not only technically accurate but also clinically relevant, ethical and effective in improving patient outcomes.