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Abstract

The early detection of diseases plays a crucial role in improving patient outcomes, reducing healthcare costs, and enabling timely interventions. In recent years, the integration of Artificial Intelligence (AI) and Predictive Analytics (PA) has emerged as a transformative approach in healthcare, offering significant advancements in detecting diseases at their earliest stages. This paper provides a comprehensive review of the application of AI-driven predictive analytics in early disease detection, focusing on various AI techniques such as machine learning (ML), deep learning (DL), natural language processing (NLP), and neural networks. These techniques have shown exceptional promise in identifying patterns and correlations within medical data—including electronic health records (EHRs), medical imaging, genetic data, and wearable devices—that can signal the onset of diseases before they become clinically evident. The paper discusses the effectiveness of AI-based predictive models in detecting a wide range of diseases, including cancer, cardiovascular diseases, diabetes, neurological disorders, neonatal conditions, and infectious diseases. Special attention is given to AI applications in neonatal healthcare, where early detection of conditions such as neonatal sepsis, respiratory distress syndrome, and congenital anomalies can significantly improve survival rates and long-term health outcomes. By leveraging large datasets and advanced algorithms, AI systems can provide accurate predictions, risk assessments, and personalized treatment plans, leading to improved early diagnosis and targeted interventions. However, the integration of AI in disease detection also presents challenges such as data privacy concerns, model interpretability, ethical issues, and the need for robust regulatory frameworks. Furthermore, the paper highlights key advancements in AI technologies that have contributed to the success of predictive analytics in healthcare, along with real-world applications, case studies, and examples of AI models that have been implemented in clinical settings. The limitations and potential solutions to these challenges are also examined, with an emphasis on the importance of high-quality, representative datasets and continuous collaboration between AI researchers, clinicians, and regulatory bodies. This review aims to provide a thorough understanding of the current landscape of AI-powered predictive analytics for early disease detection and to highlight future directions in the field. As AI technologies continue to evolve, their role in enhancing early disease detection, particularly in neonatal care, improving patient outcomes, and enabling preventive healthcare will become increasingly significant, ultimately leading to a more efficient, effective, and equitable healthcare system.