Neural Network-Based Predictive Models for Healthcare Diagnostics: Current Trends, Techniques, and Challenges
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I4P103Keywords:
Neural networks, healthcare diagnostics, data quality, interpretability, explainability, ethical considerations, data privacy, security measures, AI-driven analytics, predictive modelingAbstract
Neural network-based predictive models have revolutionized the field of healthcare diagnostics by offering advanced capabilities in disease prediction, early diagnosis, and personalized treatment plans. This paper provides a comprehensive review of the current trends, techniques, and challenges associated with the application of neural networks in healthcare diagnostics. We begin by discussing the fundamental concepts of neural networks and their evolution in the healthcare domain. We then delve into various neural network architectures, including feedforward networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), and their specific applications in healthcare. The paper also explores the integration of neural networks with other machine learning techniques and data sources, such as Electronic Health Records (EHRs) and medical imaging. We highlight key case studies and empirical results to illustrate the effectiveness of these models. Finally, we discuss the challenges and future directions, including ethical considerations, data privacy, and the need for robust validation and regulatory frameworks
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