Early Disease Detection and Prediction using AI Technologies: Approaches, Future Outlook, Mitigation Strategies, and Synthesis of Systematic Reviews
Keywords:
Machine Learning, Deep Learning, CNN, AI, RFAbstract
This paper offers an extensive and perceptive analysis of the present state of healthcare prediction. It underscores the significant benefits that have arisen from the integration of artificial intelligence, emphasizing its positive impact. The utilization of AI in healthcare prediction has brought significant advancements, but it also comes with its own set of challenges. This article aims to contribute to the advancement of disease detection and prediction by presenting the findings of an in-depth literature review encompassing recent research articles in the field. It also explores the potential impact of these findings. HealthCare prediction has become crucial for saving lives, and intelligent systems have emerged to analyse complex data relationships and generate valuable information for predictions. The paper reviewed many working papers and provided insights into the methodologies employed in each study. Additionally, it acknowledges the challenges that must be addressed to maximize the potential of artificial intelligence in disease diagnosis and prediction, and also it suggests the solution for challenges. Research has demonstrated that AI plays a significant role in accurate disease diagnosis, healthcare anticipation, and analysis of health data by leveraging large-scale clinical records and reconstructing patients' medical histories..
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