INTERPRETING CLINICAL RESEARCH FOR DISEASE DIAGNOSIS: A MACHINE LEARNING PERSPECTIVE

Abstract

This review article examines the intersection of clinical research interpretation and machine learning in disease diagnosis. By synthesizing current literature, the paper outlines how diverse clinical data, including electronic health records, medical imaging, and laboratory results, are harnessed through various machine learning approaches. Emphasis is placed on deep learning architectures, such as recurrent and convolutional neural networks, which have demonstrated promising results in early detection and predictive modeling of diseases. The review presents key case studies, including applications in diabetic retinopathy detection, pneumonia diagnosis, and heart failure prediction, while discussing the benefits and challenges associated with data heterogeneity, model interpretability, and computational scalability. Finally, the article highlights future research directions that focus on data integration, transfer learning, and the development of explainable AI frameworks, aiming to bridge the gap between innovative computational methods and their practical deployment in clinical settings. Keywords: Machine Learning; Artificial Intelligence; Clinical Research; Diagnosis; Disease Diagnosis; Deep Learning; Electronic Health Records; Medical Imaging; Predictive Modeling; Explainable AI; Scalability; Neural Networks.

More by Dinara Kaibassova

• A Reproducible Hybrid AI Framework for Early Soil Nutrient Screening from Sentinel-2 Remote Sensing DataTechnologies · 2026
• A DATASET OF KAZAKH HANDWRITTEN CHARACTERSVestnik Torajgyrov universiteta. Seriâ: fizika, matematika i kompʹûternye nauki · 2026
• Evaluation Protocol for Morphology Tolerant Predictive Text in Kazakh Using N Gram ModelsIEEE International Conference on Smart Information Systems and Technologies · 2026
• COMPARATIVE ANALYSIS OF DEEP LEARNING MODELS FOR CHEST DISEASE DIAGNOSIS USING NIH X-RAY DATASETScientific Journal of Astana IT University · 2026